Let's learn about Data Science via these 500 free blog posts. They are ordered by HackerNoon reader engagement data. Visit the Learn Repo or LearnRepo.com to find the most read blog posts about any technology.
The science of using computer programs to sift through thousands of data points and then using computer programs to present that data in a visual format.
1. 13 Best Datasets for Power BI Practice
In 2022, Gartner named Microsoft Power BI the Business Intelligence and Analytics Platforms leader. These are the 13 Best Datasets for Power BI Practice.
2. Import JSON To Google Sheets - 3 Best Ways To Do It
3 ways to pull JSON data into a Google Spreadsheet
3. Android Devices in Enterprise Mobility — Navigating Key Risks
Mobile phones have always been a staple of corporate communication. In the early days, companies would provide mobile devices to their employees.
4. How I built a spreadsheet app with Python to make data science easier
Today I'm open sourcing "Grid studio", a web-based spreadsheet application with full integration of the Python programming language.
5. How To Plot A Decision Boundary For Machine Learning Algorithms in Python
Classification algorithms learn how to assign class labels to examples (observations or data points), although their decisions can appear opaque.
6. Random Forest Regression in R: Code and Interpretation
This story looks into random forest regression in R, focusing on understanding the output and variable importance.
7. Search Algorithms in Artificial Intelligence
There can be one or many solutions to a given problem, depending on the scenario, As there can be many ways to solve that problem. <strong><em>Think about how do you approach a problem.</em></strong> Lets say you need to do something straight forward like a math multiplication. Clearly there is one correct solution, but many algorithms to multiply, depending on the size of the input. Now, take a more complicated problem, like playing a game(imagine your favorite game, chess, poker, call of duty, DOTA, anything..). In most of these games, at a given point in time, you have multiple moves that you can make, and you choose the one that gives you best possible outcome. In this scenario, there is no one correct solution, but there is a best possible solution, depending on what you want to achieve. Also, there are multiple ways to approach the problem, based on what strategy you choose to have for your game play.
8. 160+ Data Science Interview Questions
A typical interview process for a data science position includes multiple rounds. Often, one of such rounds covers theoretical concepts, where the goal is to determine if the candidate knows the fundamentals of machine learning.
9. Data Science for Portfolio Optimization: Markowitz Mean-Variance Theory
The theory formulates a mathematical model to optimize the asset allocations to gain the maximum return for a given risk-level.
10. 9 Best Data Engineering Courses You Should Take in 2023
In this listicle, you'll find some of the best data engineering courses, and career paths that can help you jumpstart your data engineering journey!
11. 10 Best Datasets for Time Series Analysis
In order to understand how a certain metric varies over time and to predict future values, we will look at the 10 Best Datasets for Time Series Analysis.
12. NLP Tutorial: Topic Modeling in Python with BerTopic
Topic modeling is an unsupervised machine learning technique that can automatically identify different topics present in a document (textual data). Data has become a key asset/tool to run many businesses around the world. With topic modeling, you can collect unstructured datasets, analyzing the documents, and obtain the relevant and desired information that can assist you in making a better decision.
13. How to Build a Web Scraper With Python [Step-by-Step Guide]
On my self-taught programming journey, my interests lie within machine learning (ML) and artificial intelligence (AI), and the language I’ve chosen to master is Python.
14. My Notes on MAE vs MSE Error Metrics 🚀
We will focus on MSE and MAE metrics, which are frequently used model evaluation metrics in regression models.
15. How To Import External Data Into Google Sheets Without Copy/Paste
Learn how to save time and eliminate manual data imports in Google Sheets by automatically connecting and importing data from external sources.
16. What is Image Annotation? – An Intro to 5 Image Annotation Services
Image annotation is one of the most important tasks in computer vision. With numerous applications, computer vision essentially strives to give a machine eyes – the ability to see and interpret the world. At times, machine learning projects seem to unlock futuristic technology we never thought possible. AI-powered applications like augmented reality, automatic speech recognition, and neural machine translation have the potential to change lives and businesses around the world. Likewise, the technologies that computer vision can give us (autonomous vehicles, facial recognition, unmanned drones) are extraordinary.
17. How To Scrape Google With Python
Ever since Google Web Search API deprecation in 2011, I've been searching for an alternative. I need a way to get links from Google search into my Python script. So I made my own, and here is a quick guide on scraping Google searches with requests and Beautiful Soup.
18. Rational Agents for Artificial Intelligence
There are multiple approaches that you might take to create Artificial Intelligence, based on what we hope to achieve with it and how will we measure its success. It ranges from extremely rare and complex systems, like self driving cars and robotics, to something that is a part of our daily lives, like face recognition, machine translation and email classification.
19. Automatic Feature Selection in Python: An Essential Guide
Feature Selection in python is the process where you automatically or manually select the features in the dataset that contribute most to your prediction.
20. Top C/C++ Machine Learning Libraries For Data Science
Importance of C++ in Data Science and Big Data
21. How to Scrape Data from Google Maps Using Python
Learn how to easily extract valuable information from Google Maps using Python with our step-by-step guide.
22. Intro to Audio Analysis: Recognizing Sounds Using Machine Learning
23. Top 20 Image Datasets for Machine Learning and Computer Vision
Computer vision enables computers to understand the content of images and videos. The goal in computer vision is to automate tasks that the human visual system can do.
24. 7 Effective Ways to Deal With a Small Dataset
In a real-world setting, you often only have a small dataset to work with. Models trained on a small number of observations tend to overfit and produce inaccurate results. Learn how to avoid overfitting and get accurate predictions even if available data is scarce.
25. Advantages and Disadvantages of Big Data
Big data may seem like any other buzzword in business, but it’s important to understand how big data benefits a company and how it’s limited.
26. How to Authenticate a User via Face Recognition in Your Web Application
Facial recognition-based authentication to verify a user in a web application is discussed in a beginner-friendly manner using FaceIO APIs.
27. How To use Google Colab with VS Code
Google Colab and VS Code are popular editor tools. Learn how you can use Google Colab with VS Code and take advantage of a full-fledged code editor.
28. How to Transform Your Data Into a Voice AI Knowledge Assistant
RAIN executives give a full breakdown of the build out and power of AI Voice Assistants.
29. THE BEST Photo to 3D AI Model !
As if taking a picture wasn’t a challenging enough technological prowess, we are now doing the opposite: modeling the world from pictures. I’ve covered amazing AI-based models that could take images and turn them into high-quality scenes. A challenging task that consists of taking a few images in the 2-dimensional picture world to create how the object or person would look in the real world.
30. Finding The Most Important Sentences Using NLP & TF-IDF
We’re going to use Term Frequency — Inverse Document Frequency (TF-IDF) to find the most important sentences in a BBC news article. Then we are going to implement this algorithm into a quick & easy Firefox extension.
31. 🎁 Releasing “Supervisely Person” dataset for teaching machines to segment humans
Hello, Machine Learning community!
32. Top 10 JavaScript Charting Libraries for Every Data Visualization Need
There're numerous JavaScript charting libraries. To make your life easier, I decided to share my picks. Check out the best JS libraries for creating web charts!
33. NLP Datasets from HuggingFace: How to Access and Train Them
The Datasets library from hugging Face provides a very efficient way to load and process NLP datasets from raw files or in-memory data. These NLP datasets have been shared by different research and practitioner communities across the world.
34. Python for Data Science: How to Scrape Website Data via the Internet's Top 300 APIs
35. 10 Machine Learning, Data Science, and Deep Learning Courses for Programmers in 2020
A curated list of courses to learn data science, machine learning, and deep learning fundamentals.
36. 14 Open Datasets for Text Classification in Machine Learning
Text classification datasets are used to categorize natural language texts according to content. For example, think classifying news articles by topic, or classifying book reviews based on a positive or negative response. Text classification is also helpful for language detection, organizing customer feedback, and fraud detection. Though time consuming when done manually, this process can be automated with machine learning models. The result saves companies time while also providing valuable data insights.
37. Pornhub Growth Hack During Coronavirus Pandemic
The 2019–20 coronavirus pandemic is an ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak was first identified in Wuhan, Hubei, China, in December 2019, and was recognized as a pandemic by the World Health Organization (WHO) on 11 March 2020.
38. 6 Biggest Limitations of Artificial Intelligence Technology
While the release of GPT-3 marks a significant milestone in the development of AI, the path forward is still obscure. There are still certain limitations to the technology today. Here are six of the major limitations facing data scientists today.
39. 10 Biggest Image Datasets for Computer Vision
Data is very important in building computer vision models and these are the 10 Biggest Datasets for Computer Vision.
40. 11 Best Climate Change Datasets for Data Science Projects
Data is a central piece of the climate change debate. With the climate change datasets on this list, many data scientists have created visualizations and models to measure and track the change in surface temperatures, sea ice levels, and more. Many of these datasets have been made public to allow people to contribute and add valuable insight into the way the climate is changing and its causes.
41. Top 10 Open Datasets for Linear Regression
On Hacker Noon, I will be sharing some of my best-performing machine learning articles. This listicle on datasets built for regression or linear regression tasks has been upvoted many times on Reddit and reshared dozens of times on various social media platforms. I hope Hacker Noon data scientists find it useful as well!
42. Technical Data Science Interview Questions: SQL and Coding
A data science interview consists of multiple rounds. One of such rounds involves theoretical questions, which we covered previously in 160+ Data Science Interview Questions.
43. Using Flask to Build a Rule-based Chatbot in Python
Learn to build AI ruled-based chatbot with a simple tutorial that can be showcased in your Portfolio.
44. Why Are We Teaching Pandas Instead of SQL?
How I learned to stop using pandas and love SQL.
45. 16 SQL Techniques Every Beginner Needs to Know
This blog post explains the most intricate data warehouse SQL techniques in detail.
46. DreamFusion: An AI that Generates 3D Models from Text
Here’s DreamFusion, a new Google Research model that can understand a sentence enough to generate a 3D model of it.
47. How to Create Dummy Data in Python
Dummy data is randomly generated data that can be substituted for live data. Whether you are a Developer, Software Engineer, or Data Scientist, sometimes you need dummy data to test what you have built, it can be a web app, mobile app, or machine learning model.
48. Scraping Tweet Replies with Python and Tweepy Twitter API [A Step-by-Step Guide]
A Quick Method To Extract Tweets and Replies For Free
49. 5 Best Stock Market APIs in 2024: A Guide for Data Scientists & Algorithmic Traders
Best stock market data APIs for data scientists and algorithmic traders: Alpha Vantage, Barchart OnDemand, Tradier, Intrinio, and Xignite.
50. 10 Best Stock Market Datasets for Machine Learning
For those looking to build predictive models, this article will introduce 10 stock market and cryptocurrency datasets for machine learning.
51. Crunching Large Datasets Made Fast and Easy: the Polars Library
Processing large data, e.g. for cleansing, aggregation or filtering is done blazingly fast with the Polars data frame library in python thanks to its design.
52. America's Secret Pager Giant
Early January 2022, I spontaneously bought a pager. I looked into the US pager market, and to my surprise...
53. Python: Updating and Appending pandas DataFrame using Dictionary
Get savvy with Pandas DataFrame updates & appends using dictionaries for smoother data tinkering.
54. AI is Neither the Magical Replacement for Human Analysts Nor a Useless Gimmick
AI is being used to help analysts with routine tasks. But it can also be a real contender on the analytics team.
55. Building A Machine Learning Model With PySpark [A Step-by-Step Guide]
Spark is the name of the engine to realize cluster computing while PySpark is the Python's library to use Spark.
56. A list of artificial intelligence tools you can use today — for businesses (2/3)
57. Build Your Own Voice Recognition Model with Tensorflow
While I'm usually a JavaScript person, there are plenty of things that Python makes easier to do. Doing voice recognition with machine learning is one of those.
58. How to Web Scrape Using Python, Snscrape & HarperDB
Learn how to execute web scraping on Twitter using the snsscrape Python library and store scraped data automatically in database by using HarperDB.
59. Multicollinearity and Its Importance in Machine Learning
Multicollinearity refers to the high correlation between two or more explanatory variables, i.e. predictors. It can be an issue in machine learning too.
60. Overview of Exploratory Data Analysis With Python
61. How to Query Deeply Nested JSON Data in PSQL
Recently I had to write a script, which should’ve changed some JSON data structure in a PSQL database. Here are some tricks I learned along the way.
62. 3 Types of Anomalies in Anomaly Detection
An Introduction to Anomaly Detection and Its Importance in Machine Learning
63. Advanced Algorithms: Median in Sliding Window
Explore the intricacies of calculating median statistics in sliding windows, a vital tool for real-time data analysis in diverse fields.
64. Using Hashcat Tool for Microsoft Active Directory Password Analysis and Cracking
Let's conduct a penetration testing on a file with a detailed study analysis of system passwords as part of an ethical hacking engagement.
65. DataOps: the Future of Data Engineering
Explore the evolution of DataOps in data engineering, its parallels with DevOps, challenges it addresses, and best practices. Transformative future of DataOps.
66. Eliminating Difference Between Business Intelligence analysts, Data Analysts or Data Scientists 🚀
There was a time when the data analyst on the team was the person driving digitalization in an adventurous data quest...and then the engineers took over.
67. Types of Linear Regression
Linear Regression is generally classified into two types:
68. Basic Understanding of ARIMA/SARIMA vs Auto ARIMA/SARIMA using Covid-19 Data Predictions
Motivation
69. 6 Best Python-Based Data Science Frameworks
70. How POST Requests with Python Make Web Scraping Easier
To scrape a website, it’s common to send GET requests, but it's useful to know how to send data. In this article, we'll see how to start with POST requests.
71. Driver Drowsiness Detection System: A Python Project with Source Code
Drowsiness detection is a safety technology that can prevent accidents that are caused by drivers who fell asleep while driving.
72. The Best 50 Sites to Learn About Data Science
73. How to Keep Your Machine Learning Models Up-to-Date
Performant machine learning models require high-quality data. And training your machine learning model is not a single, finite stage in your process. Even after you deploy it in a production environment, it’s likely you will need a steady stream of new training data to ensure your model’s predictive accuracy over time.
74. 9 Best Machine Learning, AI, and Data Science Internships in 2022
Here are the Top 9 ML, AI, and Data Science Internships to consider for 2022 if you want to get into any of these very lucrative fields in computer science.
75. How to Use Streamlit and Python to Build a Data Science App
Web apps are still useful tools for data scientists to present their data science projects to the users. Since we may not have web development skills, we can use open-source python libraries like Streamlit to easily develop web apps in a short time.
76. Never the One to Die: Persistent Data with a CDC MinIO Sink for CockroachDB
When you absolutely need to have a perfect replica of your data for data exploration, CockroachDB and MinIO is your winning strategy.
77. Karate Club a Python library for graph representation learning
Karate Club is an unsupervised machine learning extension library for the NetworkX Python package. See the documentation here.
78. Introduction To Maths Behind Neural Networks
Today, with open source machine learning software libraries such as TensorFlow, Keras or PyTorch we can create neural network, even with a high structural complexity, with just a few lines of code. Having said that, the Math behind neural networks is still a mystery to some of us and having the Math knowledge behind neural networks and deep learning can help us understand what’s happening inside a neural network. It is also helpful in architecture selection, fine-tuning of Deep Learning models, hyperparameters tuning and optimization.
79. How to Create an Engaging README for Your Data Science Project on Github
The README file is the very first item that developers examine when they access your Data Science project hosted on GitHub. Every developer should begin their exploration of your Data Science project by reading the README file. This will tell them everything they need to know, including how to install and use your project, how to contribute (if they have suggestions for improvement), and everything else.
80. Is GPU Really Necessary for Data Science Work?
A big question for Machine Learning and Deep Learning apps developers is whether or not to use a computer with a GPU, after all, GPUs are still very expensive. To get an idea, see the price of a typical GPU for processing AI in Brazil costs between US $ 1,000.00 and US $ 7,000.00 (or more).
81. 20 Best Machine Learning Resources for Data Scientists
Whether you’re a beginner looking for introductory articles or an intermediate looking for datasets or papers about new AI models, this list of machine learning resources has something for everyone interested in or working in data science. In this article, we will introduce guides, papers, tools and datasets for both computer vision and natural language processing.
82. The Best (and Worst) Punny Jokes Only Data Scientists Will Understand
For the first KDnuggets post on Hacker Noon, we bring you a lighter fare of very nerdy computer humor from the series of self-referential jokes started on Twitter earlier this week. Here are some of our favorites.
If you do understand all of the jokes, then you congratulate yourself on having excellent knowledge of Data Science and Machine Learning! If you have actually laughed at 2 or more jokes, then you have earned MS in Computer Humor! If you just smirked, you probably have a Ph.D. And I have a great joke about AGI, but it will be ready in 10 years.
Enjoy, and if you have more, add them in comments below!
Yann LeCun, @ylecun
83. How To Deploy Grafana Loki and Save Data to MinIO Using Docker Containers or Direct from Source
Although commonplace, logs hold critical information about system operations and are a valuable source of debugging and troubleshooting information.
84. How to Perform Emotion detection in Text via Python
In this tutorial, I will guide you on how to detect emotions associated with textual data and how can you apply it in real-world applications.
85. How to Use MinIO as External Tables to Extend Snowflake
MinIO is a high-performance, cloud native object store. Because of this, MinIO can become the global datastore for Snowflake customers, wherever their data sits
86. How GPUs are Beginning to Displace Clusters for Big Data & Data Science
More recently on my data science journey I have been using a low grade consumer GPU (NVIDIA GeForce 1060) to accomplish things that were previously only realistically capable on a cluster - here is why I think this is the direction data science will go in the next 5 years.
87. 12 Mistakes that Data Scientists Make and How to Avoid Them
Data analytics can transform how businesses operate. With companies having tons of data today , data analytics can help companies deliver valuable products and services to customers.
88. Reinforcement Learning: 10 Real Reward & Punishment Applications
In Reinforcement Learning (RL), agents are trained on a reward and punishment mechanism. The agent is rewarded for correct moves and punished for the wrong ones. In doing so, the agent tries to minimize wrong moves and maximize the right ones.
89. Using LTV Modeling for Quick Evaluation of Customer Acquisition Channels
True story from retail finance about LTV modeling with ML algorithms for evaluation customer acquisition channels.
90. Setting Up MinIO With Quickwit
MinIO is the right choice for Quickwit because of its industry-leading performance and scalability.
91. How to Build an Image Search Engine to Find Similar Images
After reading this article, you will be able to create a search engine for similar images for your objective from scratch
92. Size Does Matter: Global Control Group for a Bank
Learn how to approach data-driven measurement properly. See what unexpected results we got in a bank and get insights for your own data analytics journey.
93. 10 Best Hugging Face Datasets for Building NLP Models
Hugging Face offers solutions and tools for developers and researchers. This article looks at the Best Hugging Face Datasets for Building NLP Models.
94. Your Definitive Guide to Lakehouse Architecture with Iceberg and MinIO
This post focuses on how Iceberg and MinIO complement each other and how various analytic frameworks (Spark, Flink, Trino, Dremio, Snowflake) can leverage them.
95. Combining Delta Lake With MinIO for Multi-Cloud Data Lakes
The combination of MinIO and Delta Lake enables enterprises to have a multi-cloud data lake that serves as a consolidated single source of truth.
96. The Real Reasons Why AI is Built on Object Storage
From no limits on unstructured data to having greater control over serving models, here are some reasons why AI is built on object storage.
97. Implementation of Data Preprocessing on Titanic Dataset
98. A Quick Comparison of Streamlit, Dash, Reflex and Rio
Streamlit, Dash, Reflex and Rio. A comparison of python web app frameworks .
99. Meta's New Model OPT is an Open-Source GPT-3
We’ve all heard about GPT-3 and have somewhat of a clear idea of its capabilities. You’ve most certainly seen some applications born strictly due to this model, some of which I covered in a previous video about the model. GPT-3 is a model developed by OpenAI that you can access through a paid API but have no access to the model itself.
100. An Intro to No-Code Web Scraping
Web scraping has broken the barriers of programming and can now be done in a much simpler and easier manner without using a single line of code.
101. 10 Best Image Classification Datasets for ML Projects
To help you build object recognition models, scene recognition models, and more, we’ve compiled a list of the best image classification datasets. These datasets vary in scope and magnitude and can suit a variety of use cases. Furthermore, the datasets have been divided into the following categories: medical imaging, agriculture & scene recognition, and others.
102. How To Deploy Metabase on Google Cloud Platform (GCP)?
Metabase is a business intelligence tool for your organisation that plugs in various data-sources so you can explore data and build dashboards. I'll aim to provide a series of articles on provisioning and building this out for your organisation. This article is about getting up and running quickly.
103. How We Automated the Verification of Car Photos
You will find out how we at inDrive currently handle regular vehicle verifications. Every month, we ask our users to take a picture of their car.
104. So you think you know what is Artificial Intelligence?
When you think of Artificial Intelligence, the first thing that comes to mind is either Robots or Machines with Brains or Matrix or Terminator or Ex Machina or any of the other amazing concepts having machines that can think. This is an appropriate but vague understanding of Artificial Intelligence. In this article we’ll see what A.I. really is and how the definition has changed in the past.
105. Introductory Guide To Real-time Object Detection with Python
Researchers have been studying the possibilities of giving machines the ability to distinguish and identify objects through vision for years now. This particular domain, called Computer Vision or CV, has a wide range of modern-day applications.
106. Developing Streaming Data Lakes with Hudi and MinIO
Using MinIO for Hudi storage paves the way for multi-cloud data lakes and analytics.
107. How to Create a Simple Web Dashboard for Efficient Data Analytics
Dashboard with different visualizations allows you to compare data and show changes and tendencies. In this tutorial I wil explain why and how to build one.
108. Top 10 Data Science Project Ideas for 2020
As an aspiring data scientist, the best way for you to increase your skill level is by practicing. And what better way is there for practicing your technical skills than making projects.
109. Dear Aspiring Data Scientists: Skip the Certificates, Do This Instead
If you've been on LinkedIn anytime in the past several months, you've probably come across the infamous "certification post."
110. Going From Not Being Able To Code To Deep Learning Hero
111. How to Do Speech Recognition in Python
In my free time, I am attempting to build my own smart home devices. One feature they will need is speech recognition. While I am not certain yet as to how exactly I want to implement that feature, I thought it would be interesting to dive in and explore different options. The first I wanted to try was the SpeechRecognition library.
112. How ColBERT Helps Developers Overcome the Limits of RAG
Learn about ColBERT, a new way of scoring passage relevance using a BERT language model that substantially solves the problems with dense passage retrieval.
113. Why Every Software Engineer Should Learn Python?
114. MongoDB: Exploring Data Visualization Tools and Techniques
Looking for MongoDB data visualization tool? There are plenty of options but firstly its better to explore what kinds of solutions there are on the market.
115. Data Analytics 101: Your First Steps Into a Data-Driven World
Every business has its goals and the path to attaining those goals usually lies in data, it’s why our data is so important today.
116. Training Your Models on Cloud TPUs in 4 Easy Steps on Google Colab
You have a plain old TensorFlow model that’s too computationally expensive to train on your standard-issue work laptop. I get it. I’ve been there too, and if I’m being honest, seeing my laptop crash twice in a row after trying to train a model on it is painful to watch.
117. What is an RNN (Recurrent Neural Network) in Deep Learning?
RNN is one of the popular neural networks that is commonly used to solve natural language processing tasks.
118. Python Bootcamp For ML
119. Apache Druid, TiDB, ClickHouse, or Apache Doris? A Comparison of OLAP Tools
The OLAP experience of an automobile manufacturer.
120. Must-Know Base Tips for Feature Engineering With Time Series Data
Master key time series feature engineering techniques to enhance predictive models in finance, healthcare & more with our comprehensive guide.
121. Pynecone: Web Apps in Pure Python
Pynecone is an open-source framework to build web apps in pure Python and deploy with a single command.
122. 5 Best Sentiment Analysis Companies and Tools for Machine Learning
Looking for sentiment analysis companies or sentiment annotation tools? If so, you’ve come to the right place. This guide will briefly explain what sentiment analysis is, and introduce companies that provide sentiment annotation tools and services.
123. Deepmind May Have Just Created the World's First General AI
Gato from DeepMind was just published! It is a single transformer that can play Atari games, caption images, chat with people, control a real robotic arm, and more! Indeed, it is trained once and uses the same weights to achieve all those tasks. And as per Deepmind, this is not only a transformer but also an agent. This is what happens when you mix Transformers with progress on multi-task reinforcement learning agents.
124. Automating Cloud Storage Deployments With MinIO and SystemD
With the help of SystemD and MinIO you can automate your cloud object storage deployments and ensure the service lifecycle is managed smoothly and successfully.
125. Using MinIO to Build a Retrieval Augmented Generation Chat Application
Building a production-grade RAG application demands a suitable data infrastructure to store, version, process, evaluate, and query chunks of data.
126. The History of JSON and the People That Created It
Douglas Crockford and Chip Morngingstar created the data exchange format that is now known as JSON.
127. Facial Recognition Comparison with Java and C ++ using HOG
HOG - Histogram of Oriented Gradients (histogram of oriented gradients) is an image descriptor format, capable of summarizing the main characteristics of an image, such as faces for example, allowing comparison with similar images.
128. Numpy With Python For Data Science
129. 17 Open Crime Datasets for Data Science and Machine Learning Projects
For those looking to analyze crime rates or trends over a specific area or time period, we have compiled a list of the 16 best crime datasets made available for public use.
130. Top 20 Twitter Datasets for Machine Learning Projects
It is often very difficult for AI researchers to gather social media data for machine learning. Luckily, one free and accessible source of SNS data is Twitter.
131. No-Code is Eating the World
Recently, Amazon released a new tool, called Honeycode, which lets customers quickly build mobile and web applications — with no coding required. This came a few months after Google’s acquisition of the no-code mobile-app-building platform, AppSheet. While these moves surprised many, they’re in line with a larger trend I’ve observed, one that’s growing strong in all sectors, even amidst economic turmoil.
132. The Importance of Hypothesis Testing
Hypothesis tests are significant for evaluating answers to questions concerning samples of data.
133. 3 Data Distributions for Counts in Layman’s Terms
Counts are everywhere, so no matter your background, these data distributions will come in handy.
134. How to Deploy Machine Learning Models to the Cloud Quickly and Easily
Machine learning models are usually developed in a training environment (online or offline). And you can then deploy them and use them with live data.
135. 9 Free AI Tools Everyone Needs to Try
Unlock the power of AI with these 9 free tools! Boost productivity, improve decision-making, & enhance your personal life.
136. Essential Guide to Transformer Models in Machine Learning
Transformer models have become the defacto standard for NLP tasks. As an example, I’m sure you’ve already seen the awesome GPT3 Transformer demos and articles detailing how much time and money it took to train.
137. How LZ77 Data Compression Works
How does the ZIP format work?
138. Architecting Trustworthy Healthcare Data Platforms Using Declarative Pipelines
In Digital Healthcare data platforms, data quality is no longer a nice-to-have — it is a hard requirement.
139. Exploring Machine Learning Techniques for LTV/CLV Prediction
Using ML to analyze and predict CLV offers more accurate, actionable insights by learning from behavioral data at scale.
140. 8 Best Human Behaviour Datasets for Machine Learning
Human behaviour describes how people interact and in this article, we will look at the 8 Best Human Behaviour Datasets for Machine Learning.
141. How To Build and Deploy an NLP Model with FastAPI: Part 1
Learn how to build an NLP model and deploy it with a fast web framework for building APIs called FastAPI.
142. Top Dev Jokes Of 2019
Having fun while developing is necessary for programmers and developers. No matter how much serious or tough the situation is, one should always take things lightly when it comes to software development.
143. Data Testing for Machine Learning Pipelines Using Deepchecks, DagsHub, and GitHub Actions
A complete setup of a ML project using version control (also for data with DVC), experiment tracking, data checks with deepchecks and GitHub Action
144. A Data Scientist's Guide to Semi-Supervised Learning
Semi-supervised learning is the type of machine learning that is not commonly talked about by data science and machine learning practitioners but still has a very important role to play.
145. Linear Regression and its Mathematical implementation
What is Linear Regression ?
146. You Could Be Wrong About Probability
A quick walkthrough of the three frameworks in probability viz. classical, frequentist and Bayesian through an example.
147. Knowledge Graphs Gain Traction as AI Pushes Beyond Traditional Data Models
Is graph really the new star schema? What do graphs like to non-insiders, and what attracts them to the community, methodologies, applications, and innovation?
148. My Favorite Free Excel Courses for Programmers, Data Analysts, and IT Professionals
If you want to learn Microsoft Excel, a productivity tool for IT professionals, and looking for free online courses, then you have come to the right place.
149. 21 Best Coursera Courses and Certificates for IT Professionals to Learn Data Science and Cloud
Here are the top 20 Coursera Courses and Certifications to Learn Data Science, Cloud Computing, and Python.
150. How Machine Learning is Used in Astronomy
Is Astronomy data science?
151. Increase The Size of Your Datasets Through Data Augmentation
Access to training data is one of the largest blockers for many machine learning projects. Luckily, for various different projects, we can use data augmentation to increase the size of our training data many times over.
152. A Practical Guide to Machine Learning for Business
A practical guide to using machine learning in business, from defining problems and choosing models to deployment, monitoring, and delivering real value.
153. How to Build Your Own PyTorch Neural Network Layer from Scratch
This is actually an assignment from Jeremy Howard’s fast.ai course, lesson 5. I’ve showcased how easy it is to build a Convolutional Neural Networks from scratch using PyTorch. Today, let’s try to delve down even deeper and see if we could write our own nn.Linear module. Why waste your time writing your own PyTorch module while it’s already been written by the devs over at Facebook?
154. Architecting a Modern Data Lake in a Post-Hadoop World
This paper talks to the rise and fall of Hadoop HDFS and why high-performance object storage is a natural successor in the big data world.
155. A Look Into 5 Use Cases for Vector Search from Major Tech Companies
A deep dive into 5 early adopters of vector search- Pinterest, Spotify, eBay, Airbnb and Doordash- who have integrated AI into their applications.
156. Hinge Loss - A Steadfast Loss Evaluation Function for the SVM Classification Models in AI & ML
Researchers use an algebraic acme called “Losses” in order to optimise the machine learning space defined by a specific use case.
157. A Comprehensive Guide to Building DolphinScheduler 3.2.0 Production-Grade Cluster Deployment
In version 3.2.0, DolphinScheduler introduces a series of new features and improvements, significantly enhancing its stability.
158. 10 Best Datasets for Geospatial Analytics (Open and Public Access)
Scientists use geospatial analytics to build visualizations such as maps, graphs and cartograms. These are the Best Public Datasets for Geospatial Analytics.
159. Crowdsourcing Data Labeling for Machine Learning Projects [A How-To Guide]
Research suggests that data scientists spend a whopping 80% of their time preprocessing data and only 20% on actually building machine learning models. With that in mind, it’s no wonder why the machine learning community was quick to embrace crowdsourcing for data labeling. Crowdsourcing helps break down large and complex machine learning problems into smaller and simpler tasks for a large distributed workforce.
160. How to Visualize Bias and Variance
In the process of building a Machine Learning model, there is a trade-off between bias and variance.
161. Introducing CatalyzeX: A Browser Extension for Machine Learning
Andrew Ng likes it, you probably will too!
162. When to Use DynamoDB Secondary Indexes
DynamoDB's secondary indexes are a powerful tool for enabling new access patterns for your data.
163. Solving Time Series Forecasting Problems: Principles and Techniques
Explore time series analysis: from cross-validation, decomposition, transformation to advanced modeling with ARIMA, Neural Networks, and more.
164. Dimensionality Reduction Using PCA : A Comprehensive Hands-On Primer
We, humans, are experiencing tailor-made services which have been engineered right for us, we are not troubled personally, but we are doing one thing every day, which is kind of helping this intelligent machine work day and night just to make sure all these services are curated right and delivered to us in the manner we like to consume it.
165. Creating a RAG Agent: Step-by-Step Guide
In this tutorial, we will develop a simple Agent that accesses multiple data sources and invokes data retrieval when needed.
166. What the Heck Is Malloy?
Malloy is a new experimental language for describing data relationships and transformations created by the developer of Looker.
167. A Guide to DynamoDB Secondary Indexes: GSI, LSI, Elasticsearch and Rockset
For analytical use cases, you can gain significant performance and cost advantages by syncing the DynamoDB table with a different tool or service like Rockset.
168. Best B2B Data Providers in 2026
This guide will look into the ten best B2B data providers that can fuel your business strategy and help you expand your customer base.
169. Answering Metric Questions in Product Manager Interviews
Product manager interviews usually include a section on metrics. As a data scientist at Uber, I’ve often given or helped friends prepare for these interviews. The difference between candidates who crush the metric questions and those who struggle turns, as far as I can tell, on whether they have a framework that they can apply.
170. Probabilistic Predictions in Classification - Evaluating Quality
Binary classification is one of the most common machine learning tasks. In practice, the goal of such tasks often extends beyond simply predicting a class.
171. How to Use Approximate Leave-one-out Cross-validation to Build Better Models
How to use Approximate leave-one-out cross-validation for hyperparameter optimization and outlier detection for logistic regression and ridge regression
172. Gain State-Of-The-Art Results on Tabular Data with Deep Learning & Embedding Layers [A How To Guide]
Tree-based models like Random Forest and XGBoost have become very popular in solving tabular(structured) data problems and gained a lot of tractions in Kaggle competitions lately. It has its very deserving reasons. However, in this article, I want to introduce a different approach from fast.ai’s Tabular module leveraging.
173. Improve Machine Learning Model Performance by Combining Categorical Features
Learn how to combine categorical features in your dataset to improve your machine learning model performance.
174. A Quick Introduction to Machine Learning with Dagster
This article is a quick introduction to Dagster using a small ML project. It is beginner friendly but might also suit more advanced programmers if they dont know Dagster.
175. Bayesian Brain: Is Your Brain a Data Scientist?
Is your Brain a Data Scientist? Yes, according to the Bayesian Brain Hypothesis, your brain is a Bayesian statistician. Let me explain.
176. How Three ML Models Transform Product Analytics
Learn how machine learning advances product analytics — from predicting behavior to optimizing personalized, data-driven decisions.
177. Data Engineering: An Interview with Meta Engineer Leonid Chashnikov
As we sit down for this exclusive interview, Leonid offers a rare glimpse into the intricate process of weaving the digital fabric that shapes our lives.
178. Data Science Toolkit (Concepts + Code)
Hi folks !! In this post, i will discuss about basic tools and software that one can use to solve a data science problem . If you are new to ML or Data Science or Statistics, Feel free to check out my other blog on ML by clicking on the link below.
179. Best Libraries That Will Assist You In EDA: 2021 Edition
Exploratory Data Analysis (EDA) is an essential step in the data science project lifecycle. Here are the top 10 python tools for EDA.
180. How We Increased Database Query Concurrency by 20 Times
Learn 5 ways to accelerate point queries and 4 methods to further improve concurrency: row storage format, short circuit, prepared statement, and row storage ca
181. How to Use Propensity Score Matching to Measure Down Stream Causal Impact of an Event
How can we know ours ads are making impact that we aim for? What if targeted ads are not working the way we want them to?
182. Train a NER Transformer Model with Just a Few Lines of Code via spaCy 3
Transformer models have become by far the state of the art in NLP technology, with applications ranging from NER, Text Classification, and Question Answering
183. What Happens When You Get Sick Right Now?
We are living in a weird time. Day by day we see more & more people coughing and getting sick, our neighbors, coworkers on Zoom calls, politicians, etc… But here’s when it becomes really, really scary — when you become one of “those” and have no clue what to do. Your reptile brain activates, you enter a state of panic, and engage complete freakout mode. That’s what happened to me this Monday, and I’m not sure I’m past this stage.
184. Navigating MySQL Data Types: Sets and Enums
Learn how to use MySQL’s SET and ENUM data types effectively. This guide explains their internal behavior, common pitfalls, and best practices
185. How to Start with Web Scraping and Why You Don't Need to Code
Collecting data from the web can be the core of data science. In this article, we'll see how to start with scraping with or without having to write code.
186. What the Heck Is Apache Paimon?
Have you heard of the data Streamhouse yet? Find out more and learn about Apache Paimon
187. Microsoft Fabric IQ Puts Ontology Back on the Map — and Back in the Confusion
Everyone is talking about ontologies. Why, what is an ontology actually, and how is it related to graphs?
188. Time Series Forecasting with TensorFlow.js
Pull stock prices from online API and perform predictions using Recurrent Neural Network & Long Short Term Memory (LSTM) with TensorFlow.js framework
189. A Roadmap For Becoming a Data Scientist
So you want to become a data scientist? You have heard so much about data science and want to know what all the hype is about? Well, you have come to the perfect place. The field of data science has evolved significantly in the past decade. Today there are multiple ways to jump into the field and become a data scientist. Not all of them need you to have a fancy degree either. So let’s get started!
190. Semantic Search Queries Return More Informed Results
191. A Guide to Scraping HTML Tables with Pandas and BeautifulSoup
How to not get stuck when collecting tabular data from the internet.
192. Exploring the Top Data Science and Machine Learning (DSML) Platforms of 2022
Exploring Data Science and Machine Learning (DSML) Platforms
193. How LZ78 Compression Algorithm Works
How does the GIF format work?
194. AI Meets Ethics: Navigating Bias and Fairness in Data Science Models
Explore a product developer's journey in tackling AI bias and fairness. Learn how ethical considerations shape AI design, ensuring technology benefits everyone.
195. Polygon data: What it is and how can it be used?
This blog explains about polygon data, its benefits and how it is widely used in geomarketing, indoor mapping, and mobility analysis for orgnaizations.
196. Google's New AI Creates Summaries of Your Documents in Google Docs
Google recently announced a new model for automatically generating summaries using machine learning, released in Google Docs that you can already use.
197. Building an AI Red Team to Stop Problems Before They Start
An incredible 87% of data science projects never go live.
198. 20 Best PyTorch Datasets for Building Deep Learning Models
PyTorch has gained a reputation as a research-focused framework, and these are the Best PyTorch Datasets for Building Deep Learning Models available today.
199. Ten Future Technologies That Aren't in the Public Eye (Yet)
CRISPR, Quantum, Graphene, Smart Dust, Digital Twins, the Metaverse… You’ve heard about it all. Seen it all. Read it all. Or have you?
200. Secrets, Relationships, and Patterns: An Introduction to Exploratory Data Analysis
In this article, we set sail on a captivating journey through the EDA process, using the legendary Titanic dataset from Kaggle as our North Star.
201. Pickling and Unpickling in Python
In this blog, you will learn about the Pickling and Unpickling process, although it is quite simple it is very important and useful.
202. 5 Best AI Articles of the Month
Here are the five best articles related to artificial intelligence in May posted on Hackernoon.
203. Uploading a 1 Million Row CSV File to the Backend in 10 Seconds
Uploading 1 million row size large CSV to mongoDB using nodejs stream
204. An Introduction to the Power of Vector Search for Beginners
An introduction to neural vector search, in comparison to keyword-based search.
205. How to detect plagiarism in text using Python
Intro
206. How to Structure a PyTorch ML Project With Google Colab and TensorBoard
Let’s build a fashion-MNIST CNN, PyTorch style. This is A Line-by-line guide on how to structure a PyTorch ML project from scratch using Google Colab and TensorBoard
207. Top 8 Best Qlik Sense Extensions
Qlik Sense is powerful data visualization and BI software. But sometimes its functions are not enough. Meet the best Qlik Sense extensions to do more with data!
208. The Best Slack Groups for Data Scientists to Join
The online data science community is supportive and collaborative. One of the ways you can join the community is to find machine learning and AI Slack groups.
209. No-Code Machine Learning inside Google Sheets
Introduction
210. Avengers Ensemble: How Ensemble Modeling Helps You Avoid Overfitting
Ensemble modelling helps you avoid overfitting by reducing variance in the prediction and minimizing modelling method bias.
211. Azure Data Factory: An Amazing Data Migration Tool
212. Why I Built a SaaS to Replace Myself
The time to start building one's synthetic replacement is now.
213. Explain Complex Concepts With Minimalistic Drawings With Okso.app
Minimalistic Data Structure Sketches
214. Must-Know Theorems for Programmers
Programming is a complex and multifaceted field that encompasses a wide range of mathematical and computational concepts and techniques.
215. How to Think Like a Data Scientist or Data Analyst
216. What Is Weaviate And How To Create Data Schemas In It
217. How I Built an Interactive Dashboard Web App to Visualize Boxing Data
I am a huge fan of combat sports, with boxing in particular being my favourite. As much as it may appear as a purely physical sport where your sole objective is to either outbox or knock your opponent out, it is far more strategic that one would expect and incorporates an element psychology. Like a chess game, each punch thrown has to be calculated, recklessly overextending yourself might leave you more vulnerable to a counter punch, while being overly passive and defensive might swing the momentum in your opponent’s favour and not get you enough points to win the fight. If you let self-doubt sink in or are intimidated by your opponent you have already lost the battle. On top of all this, you need to remain respectful of the sport and the life threatening dangers it presents. In the words of of Sugar Ray Leonard, 'you don't play boxing'.
218. Pycaret: A Faster Way to Build Machine Learning Models
Pycaret is an open-source, low code library in python that aims to automate the development of machine learning models.
219. The Pros and Cons of Collecting Online and Offline Data
220. How Data Scientists Can Become More Marketable
This headline may seem a bit odd to you. After all, if you’re a data scientist in 2019, you’re already marketable. Since data science has a huge impact on today’s businesses, the demand for DS experts is growing. At the moment I’m writing this, there are 144,527 data science jobs on LinkedIn alone.
221. 5 Top Tech Careers to Consider Studying Towards in 2021
Gain entry into IT with knowledge of data science, engineering, cloud computing, cybersecurity, or devops.
222. How to Build a Multi-label NLP Classifier from Scratch
Attacking Toxic Comments Kaggle Competition Using Fast.ai
223. How to Optimize Your Marketing Budget Using Just Three Letters: MMM
Marketing Mix Modeling is a statistical analysis method used in marketing to determine the optimal allocation of resources.
224. Quantitative ROI Modeling for Tech Product Investments
How tech executives can harness advanced econometric and AI-driven simulation techniques to make informed investment decisions under uncertainty?
225. Data Set and Data Augmentation for Face Detection and Recognition
When it comes to building an Artificially Intelligent (AI) application, your approach must be data first, not application first.
226. How to Choose the Right Database for your Requirements
Imagine — You’re in a system design interview and need to pick a database to store, let’s say, order-related data in an e-commerce system. Your data is structured and needs to be consistent, but your query pattern doesn’t match with a standard relational DB’s. You need your transactions to be isolated, and atomic and all things ACID… But OMG it needs to scale infinitely like Cassandra!! So how would you decide what storage solution to choose? Well, let’s see!
227. Building A Log Analytics Solution 10 Times More Cost-Effective Than Elasticsearch
There exist two common log processing solutions within the industry, exemplified by Elasticsearch and Grafana Loki, respectively.
228. DecentraMind for Web 3.0 or Against It? — Interview with Mikhail Danieli
DecentraMind by Web 3.0 or for it? — interview with Mikhail Danieli, project visionary and ambassador about the future of the platform and the company.
229. 5 Types of Machine Learning Algorithms You Should Know
Machine learning has become a diverse business tool to enhance the various elements of business operations. Also, it has a significant influence on the performance of the business. Machine learning algorithms are used widely to maintain competition with different industries. However, there is a different type of algorithms for goals and data sets. The selection of an algorithm depends on user role and the purpose. If you are using Linear regression, then you can quickly implement or train rather than other machine learning algorithms. But the drawback of this algorithm is that it is not applicable for complex predictions. So you should know about the different types of machine learning algorithms for getting better results.
230. Can Graph Neural Networks Solve Real-World Problems?
In this article, we will learn about GNNs and its structure as well as its applications
231. Streamline Structured + Unstructured Data Flows from Postgres with AI
Comprehensive walkthrough on using CocoIndex to build unified, incrementally updated search and analytics pipelines.
[232. Differential Privacy with Tensorflow 2.0 : Multi class Text Classification
Privacy](https://hackernoon.com/differential-privacy-with-tensorflow-20-multi-class-text-classification-privacy-yk7a37uh)
Introduction
233. 8 Best AI Conferences to Attend in 2022
Here’s the full list of top AI conferences to attend in 2022, from the most technical to business-focused to academic
234. How I Designed My Own Machine Learning and Artificial Intelligence Degree
After noticing my programming courses in college were outdated, I began this year by dropping out of college to teach myself machine learning and artificial intelligence using online resources. With no experience in tech, no previous degrees, here is the degree I designed in Machine Learning and Artificial Intelligence from beginning to end to get me to my goal — to become a well-rounded machine learning and AI engineer.
235. How To Build and Deploy an NLP Model with FastAPI: Part 2
Learn how to build an NLP model and deploy it with a fast web framework for building APIs called FastAPI.
236. Mobile Price Classification: An Open Source Data Science Project with Dagshub
Machine learning models are often developed in a training environment, which may be online or offline, and can then be deployed to be used with live data once they have been tested.
237. Python Library vs. Implementation From Scratch: 7 Things to Consider
The question of from-scratch implementation vs Python library comes up once in a while, no matter the goal of your project.
238. Mean Reversion Trading Systems and Cryptocurrency Trading [A Deep Dive]
Prices move in a wave like fashion, moving back and forth following a broader trend. While doing so, it often revolves around a mean. It might move across or bounce off the mean. Mean reversion systems are designed to exploit this tendency.
239. How to Scrape NLP Datasets From Youtube
Too lazy to scrape nlp data yourself? In this post, I’ll show you a quick way to scrape NLP datasets using Youtube and Python.
240. How Bayesian Tail-Risk Modeling can save your Retail Business Marketing Budget
Why average ROI fails. Learn how distributional and tail-risk modeling protects marketing campaigns from catastrophic losses using Bayesian methods.
241. Intro to Neural Networks: CNN vs. RNN
In machine learning, each type of artificial neural network is tailored to certain tasks. This article will introduce two types of neural networks: convolutional neural networks (CNN) and recurrent neural networks (RNN). Using popular Youtube videos and visual aids, we will explain the difference between CNN and RNN and how they are used in computer vision and natural language processing.
242. Realistic Face Manipulation in Videos With AI
You've most certainly seen movies like the recent Captain Marvel or Gemini Man where Samuel L Jackson and Will Smith appeared to look like they were much younger. This requires hundreds if not thousands of hours of work from professionals manually editing the scenes he appeared in. Instead, you could use a simple AI and do it within a few minutes.
243. Analyzing Twitter Conversations with the New Twitter V2 API
Getting actionable insights around a topic using the new Twitter API v2 endpoint
244. Basics of Machine Learning and its capabilities in Cybersecurity
The article explores Machine Learning's vital role in cybersecurity, addressing evolving digital threats. It covers ML's types, iterative process, feature engi
245. I Built a Boxing Prediction Web App on Shiny, Here's How
As part of my data-science career track bootcamp, I had to complete a few personal capstones. For this particular capstone, I opted to focus on building something I personally care about - what better way to learn and possibly build something valuable than by working on a passion project.
246. How I mastered Python in Lockdown without spending a penny
I always wanted to learn programming. Writing codes, making algorithms always excited me. Being a mechanical engineer, I was never taught these subjects in depth.
247. How To Create a Python Data Engineering Project with a Pipeline Pattern
In this article, we cover how to use pipeline patterns in python data engineering projects. Create a functional pipeline, install fastcore, and other steps.
248. 9 Free Data Science Courses & Guides For Beginners
We human beings are depending so much on digital and smart devices. And all these devices are creating data at a very fast rate. According to an article on Forbes more than 90% of the world data has been created in past 2 to 3 years.
249. What is a Data Reliability Engineer?
With each day, enterprises increasingly rely on data to make decisions.
250. Summarizing Most Popular Text-to-Image Synthesis Methods With Python
Comparative Study of Different Adversarial Text to Image Methods
251. From Satellite Signals to Neural Networks
See how Andrei Shcherbinin built production-ready ML systems with 12x faster attribution, 95% chatbot automation, and stronger monitoring.
252. What the Heck is PRQL?
Another clever tool for a powerful SQL pre-processor
[253. Diverse types of Artificial Intelligence:
A Must-know for AI Enthusiasts](https://hackernoon.com/diverse-types-of-artificial-intelligence-a-must-know-for-ai-enthusiasts)
A precursory article that explains various categorizations of artificial intelligence, some real-life examples and concepts.
254. Using a Relational Database to Query Unstructured Data
Using Relational Database to search inside unstructured data
255. Why 87% of Machine learning Projects Fail
This article will serve as a lesson on the shocking reasons for your AI adoption disaster. We see news about machine learning everywhere. Indeed, there is lot of potential in machine learning. According to Gartner’s predictions, “Through 2020, 80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organization” and Transform 2019 of VentureBeat predicted that 87% of AI projects will never make it into production.
256. BI Analyst Interview Questions And Answers: 2020 Edition
Why you should prepare for BI analyst interview questions?
257. Statistics Cheat Sheet: A Beginner's Guide to Probability and Random Events
A beginner’s guide to Probability and Random Events. Understand the key statistics concepts and areas to focus on to ace your next data science interview.
258. Anomaly Detection Strategies for IoT Sensors
259. An Internal Email to Tim Cook and the State of Business Intelligence
We get a glimpse into the inner workings of a valuable company and it turns out it's not all sunshine and rainbows.
260. What Are Convolution Neural Networks? [ELI5]
Universal Approximation Theorem says that Feed-Forward Neural Network (also known as Multi-layered Network of Neurons) can act as powerful approximation to learn the non-linear relationship between the input and output. But the problem with the Feed-Forward Neural Network is that the network is prone to over-fitting due to the presence of many parameters within the network to learn.
261. 10 Best Reddit Datasets for NLP and Other ML Projects
In this post, I wanted to share a Reddit dataset list that gained a lot of traction on social media when it was first posted.
262. SERP Analysis with Google Search Console+Python
Google makes billions and billions from paid search, but did you know those folks without adblockers are only responsible for about 6 percent of web visits? Long live free organic search, the primary diet for <a href="https://www.conductor.com/blog/2014/07/organic-search-actually-responsible-64-web-traffic/" target="_blank">almost 2/3rds of all web traffic</a>!
263. Build Your Own AI Chatbot on Your Local PC — Online and Offline
You can easily build a personal AI chatbot that runs both online (using OpenAI GPT) and offline (using Ollama local models) right from your local machine.
264. Where to Learn Machine and Deep Learning for Free
265. 13 Highest paying Tech Jobs Software Engineers can aim to increase their Pay
266. Building Handwritten Digits Recognizer using Support Vector Machine
Handwriting Recognition:
267. MLOps Engineer vs ML Engineer: The Key Differences
Discover the key differences between MLOps Engineer vs ML Engineer roles, including focus, collaboration, and tooling.
268. Context Graphs, Ontologies, and the Race to Fix Enterprise AI
What are context graphs, what are they good for, and why are they dubbed AI’s trillion-dollar opportunity? What does context mean, and how can it be defined?
269. Features Selection by Using Xverse Package
Learn how to apply a variety of techniques to select features with Xverse package.
270. How to Optimize Google Cloud BigQuery and Control the Cost
I unknowingly blew $3,000 in 6 hours on one query in Google Cloud BigQuery. Here’s why and 3 easy steps to optimize the cost.
271. Retraining Machine Learning Model Approaches
Retraining Machine Learning Model, Model Drift, Different ways to identify model drift, Performance Degradation
272. AI Facts Every Dev Should Know: Artificial intelligence is older than you, probably
The hype around AI is growing rapidly, as most research companies predict AI will take on an increasingly important role in the future.
273. COVID-19: "In God We Trust, All Others Must Bring [CLEAN] Data"
In these difficult days for all of us, I’ve heard all sorts of things. From the fake news sent through Whatsapp, like vitamin C can save your life, to holding your breath in the morning to check if you’ve been hit by COVID-19. The mantra that everyone keeps repeating is “stay at home!”, okay fine, but what exactly does “stay home” mean? The question seems ridiculous when you think of a relatively short period, 15 days? A month? But if we look critically at the situation, we surely realize that it won’t be 15 days, and it won’t be a month. It will be a long, long time. Why am I saying this? Because “stay at home” doesn’t protect us from the virus. Staying at home is to protect our health care facilities from collapse. And I’m not saying that this is wrong. I’m just saying that if we want to protect the health care system from collapse, well then we’ll stay home a long, long time. But in doing so we will irreparably damage the economic system by profoundly changing our social and political model. It is inevitable. Let’s face it and not have too many illusions.
274. The Essential Architectures For Every Data Scientist and Big Data Engineer
Comprehensive List of Feature Store Architectures for Data Scientists and Big Data Professionals
275. 5 Best Machine Learning Books for ML Beginners
Here is a list of the best books to learn machine learning for beginners to help build their careers in the ML Industry.
276. 11 Awesome (and Worrisome) Applications of AI
For years AI was touted to be the next big technology. Expected to revolutionize the job industry and effectively kill millions of human jobs, it became the poster child for job cuts. Despite this, its adoption has been increasingly well-received. To the tech experts, this wasn’t really surprising given its vast range of use cases.
277. Say Goodbye to SEO - ChatGPT Steals the Show With Smarter Search
Search Engine Optimization (SEO) has been the backbone of an online search for over two decades now. But as Artificial Intelligence (AI) technology moves quickl
278. Navigating MySQL data types: date and time
Explore the nuances of MySQL’s DATETIME and TIMESTAMP types, from handling time zones and zero dates to optimizing performance and preventing pitfalls.
279. The damaging effects of unplanned work
280. Positional Embedding: The Secret behind the Accuracy of Transformer Neural Networks
An article explaining the intuition behind the “positional embedding” in transformer models from the renowned research paper - “Attention Is All You Need”.
281. Is The Modern Data Warehouse Dead?
Do we need a radical new approach to data warehouse technology? An immutable data warehouse starts with the data consumer SLAs and pipes data in pre-modeled.
282. How To Predict Election Results using Twitter
Elections play crucial role in all democracies and social media is an important aspect in this process. Presently, political parties increasingly rely on social media platforms like Twitter and Facebook for political communication.The use of social media in political marketing campaigns has grown dramatically over the past few years. It is also expected to become even more critical to future political campaigns, as it creates two-way communication and engagement that stimulates and fosters candidates relationships with their supporters.
283. What is OpenAI's Whisper Model?
Have you ever dreamed of a good transcription tool that would accurately understand what you say and write it down? Not like the automatic YouTube translation tools… I mean, they are good but far from perfect. Just try it out and turn the feature on for the video, and you’ll see what I’m talking about.
284. How Can Machine Learning Predict the Stock Market?
Artificial intelligence is changing the world as we know it. Form self-driving cars to weather predictions. Now it's taking on the stock market. Here's how.
285. Why Many Data Science Jobs Are Actually Data Engineering
Most data science job descriptions are actually for data engineers.
286. Top 9 Free Beginner Tutorials for Machine Learning (ML)
This post includes a round-up of some of the best free beginner tutorials for Machine Learning.
287. The Evolution of Decision Trees: From Shannon Entropy to Modern Applications and Specialties
Discover the evolution and importance of decision trees in machine learning, from their early beginnings in the 1960s to their widespread use in modern ensemble
288. Use plaidML to do Machine Learning on macOS with an AMD GPU
Want to train machine learning models on your Mac’s integrated AMD GPU or an external graphics card? Look no further than PlaidML.
289. Insights Through Vision: Tracking Eyes Using OpenCV for Blink Detection
We explore the use of OpenCV and techniques like contour detection for eye blink detection, pupil tracking, Discuss the challenges and their specific Solutions.
290. 6 Biggest Differences Between Airbyte And Singer
We’ve been asked if Airbyte was being built on top of Singer. Even though we loved the initial mission they had, that won’t be the case. Aibyte's data protocol will be compatible with Singer’s, so that you can easily integrate and use Singer’s taps, but our protocol will differ in many ways from theirs.
291. Beat The Heat with Machine Learning Cheat Sheet
If you are a beginner and just started machine learning or even an intermediate level programmer, you might have been stuck on how do you solve this problem. Where do you start? and where do you go from here?
292. Software Development Tricks Coding for Beginners and More
This week on HackerNoon's Stories of the Week, we looked at three articles that covered the world of software development from employment to security.
293. AI vs ML: What's the Difference?
Learn the distinctions between AI and ML with vivid examples.
294. Why Do We Use Hexagons And Not Sqaures to Aggregate Location Data
If you are a two-degree marketplace like Uber, you cater to millions of users requesting a ride through your driver partners accepting and fulfilling those requests. For a three-degree marketplace like Swiggy, there is another static component added (like restaurants or stores), where delivery partners pick up the orders.
295. Downloading Data as a File with Alpine.js
A quick demonstration of using JavaScript to download ad hoc data.
296. A Guide to Using Apache Cassandra as a Real-time Feature Store
This guide explores real-time AI and the unique performance and cost attributes of Cassandra that make it an excellent database for a feature store.
297. Credit Card Fraud Detection via Machine Learning: A Case Study
A machine learning guide on how to identify fraudulent credit card transactions by using the PyOD toolkit.
298. Anscombe’s Quartet And Importance of Data Visualization
Anscombe’s quartet comprises four data sets that have nearly identical simple descriptive statistics, yet have very different distributions and appear very different when graphed.
— Wikipedia
299. Tencent Music Transitions from ClickHouse to Apache Doris
Evolution of our data processing architecture towards better performance and simpler maintenance at Tencent Music.
300. A Quick Introduction to Python Numpy for Beginners
This tutorial will help you get started with NumPy by teaching you to visualize multidimensional arrays.
301. How to Train Your Own Private ChatGPT Model for the Cost of a Starbucks Coffee
With the cost of a cup of Starbucks and two hours of your time, you can own your own trained open-source large-scale model.
302. Machine Learning Magic: How to Speed Up Offline Inference for Large Datasets
Running inference at scale is challenging. See how we speed up the I/O performance for large-scale ML/DL offline inference jobs.
303. Multi-Armed Bandits: The Best Reinforcement Learning Solution for Your Task
Explore the fascinating world of Reinforcement Learning through Multi-Armed Bandits (MABs), balancing exploration & exploitation.
304. Machine Learning 101: How And Where To Start For Absolute Beginners
This post covers all you will need for your Journey as a Beginner. All the Resources are provided with links. You just need Time and Your dedication.
305. What is the Future for SQL Developers in a Machine Learning World?
Do you know the machine learning global market is estimated to reach $30.6 billion by 2024? This marvellous growth is the outcome of Omni-presence of artificial intelligence and its trending subset; machine learning.
306. Self-Supervised Machine Learning: The Story So Far and Trends For 2021
Let’s talk about self-supervised machine learning - a way to teach a model a lot without manual markup, as well as an opportunity to avoid deep learning when setting a model up to solve a problem. This material requires an intermediate level of preparation; there are many references to original publications.
307. How to Improve Your Data Literacy Skills
Are you data literate? In today's data-driven world, data literacy is a crucial skill. Here's how you can develop it for yourself.
308. Training Your Own Text Classification Model From Scratch With Tensorflow Is As Easy As ABC
309. Using Monte Carlo to Explain Why You Don't Win Daily Fantasy Baseball Games
Use Monte Carlo simulation to understand the risk in fantasy baseball. Learn why optimizing a lineup is a tall order.
310. Busting Data Science Myths: "You Need a PhD, Extensive Python Skills, and Tons of Experience"
DJ Patil and Jeff Hammerbacher coined the title Data Scientist while working at LinkedIn and Facebook, respectively, to mean someone who “uses data to interact with the world, study it and try to come up with new things.”
311. How to Create a Bubble Map with JavaScript to Visualize Election Results
A beginner level tutorial to get started with data visualization by creating an interesting and intuitive JavaScript bubble map
312. Galactica is an AI Model Trained on 120 Billion Parameters
On November 15th, MetaAI and Papers with Code announced the release of Galactica, a game-changer, open-source large language model trained on scientific knowledge with 120 billion parameters.
313. ‘Data Science Is Not a Math Skill but a Life Skill’: Noonies Nominee Kirk Borne
From astrophysics to data science, here's a story of a lifetime journey with modeling the Universe and other dynamic things that move through space and time.
314. 3 New Startups That Are Innovating DeFi Data Analysis Technology
Data analysis as a whole is one of the most important industries. Now that DeFi is a full-fledged industry, there is a growing need for valuable data analytics.
315. Top 40+ Data Science Product Interview Questions
Find the top 40+ product interview questions you must prepare for your next data science interview.
316. Why Jupyter Notebooks are the Future of Data Science
How Jupyter Notebooks played an important role in the incredible rise in popularity of Data Science and why they are its future.
317. Using Real-Time Data in Digital Marketing
Learn how you can use real-time data in digital marketing for customer engagement and retention, analyze real-time data for faster decision-making
318. Covid-19: Analysing The Spread Across Populations
A large portion of mild and asymptomatic cases may go unreported. The data will never be perfect, the true cases are likely much larger as the testing frequency and effectiveness vary in different regions.
319. Understanding SQL's Application in Data Science [A Deep Dive]
To learn about SQL, we need to understand how a DBMS works. DBMS or Database Management System is essentially a software to create and manage databases.
320. What Data Scientists Should Know About Multi-output and Multi-label Training
Multi-output Machine Learning — MixedRandomForest
321. How Likely Was One to Survive on the Titanic?
Only 38% of the passengers survived this devastating event, prompting me to wonder about the individuals who were aboard the Titanic that fateful night.
322. Extract Prominent Colors from an Image Using Machine Learning
This article explains how I found a nice and simple algorithm to extract prominent colors out of an image.
323. Fixing the Cold Start Problem in Recommender Systems
A cold start problem is when the system cannot draw any inferences for users or items about which it has not yet gathered sufficient information. Simply put, if you have no or less initial data, what recommendation is the system supposed to give to the user?
While recommender systems are useful for users who have some previous interaction history, the same might not be the case for a new user or a newly added item. The problem is that in both cases we don’t have any history to base the recommendations on.
324. 5 Companies Developing Computer Vision Technology in 2020
Computer vision technology is the poster child of artificial intelligence. It is the sector of the industry that gets the most media attention because of the tools and benefits the technology can provide. From autonomous vehicles and drones to cancer detection and augmented reality, technologies that once only existed in science fiction are now at our doorstep.
325. The Simplest Way to do Exploratory Data Analysis(EDA) using Python Code
EDA for Data Analysis or Data Visualization is very important. It gives a brief summary and main characteristics of data. According to a survey, Data Scientist uses their most of time to perform EDA tasks.
326. How to Chat With Your Data Using OpenAI, Pinecone, Airbyte and Langchain: A Guide
Learn how to build an AI chat bot for your own data within 40 minutes. An end-to-end LLM tutorial.
327. A Deep Dive Into Market-Leading Blockchain Analytic Solutions
Explore the pros and cons of industry-leading blockchain analytic tools, examining how each solution handles data across the blockchain network.
328. Dataism: Idea or Ideology?
Dataism suggests that the entire universe can be interpreted as data flows and that all phenomena, including human behaviour, can be reduced to data processes.
329. How No-Code Can Rekindle Your Relationship With Data Science
A modern business user’s relationship with data is fairly complicated. It starts with curiosity. “Which of my top users will do X,Y, or Z?” You need a data output to move forward with a decision—except you’re having communication issues.
330. Behaviors Trees in AI: Why you Should Ditch Your Event Framework
In this article, I look into some of the shortages of event-driven
programming and suggest behavior trees as an effective alternative,
suitable for back/front-end application development.
331. Exploring the Intersection of Data Science and Cyber Security: Insights and Applications
Discover how data science is revolutionizing cyber security and learn about its role in detecting and preventing cyber-attacks.
332. Meet Council: The Future of AI Agents
Get to know Council, ChainML's open-source, AI agent platform.
333. 7 Types of Data Bias in Machine Learning
Data bias in machine learning is a type of error in which certain elements of a dataset are more heavily weighted and/or represented than others. A biased dataset does not accurately represent a model’s use case, resulting in skewed outcomes, low accuracy levels, and analytical errors.
334. A GAN approach To Synthetic Time-Series Data
Although sequential data is pretty common to be found and highly useful, there are many reasons that lead to not leverage it
335. Beyond A/B Testing — Switchbacks and Synthetic Control Group
Experimentation designing in the marketplace without AB-Testing using Synthetic Control Groups and Switchbacks.
336. Data Science in Finance: 5 Ways It Changed the Industry
What’s the Role of Data Science in Finance?
337. Beginner's Roadmap to Large Language Models (LLMOps) in 2023: All free!
This guide isn’t just a compilation of LLM resources; it's a curated journey through the most valuable skills in the industry.
338. Docker Use Cases: Most Common Ways to Use
Unveiling Docker's Potential in Modern IT Landscapes - An In-Depth Exploration of Applications and Best Practices.
339. Image Style Transfer And Video Transformation In EbSynth
340. About The Meteoric Rise of the Low Code Data Scientist
If you’re not already using low-code platforms, you will be very soon. Low-code is helping to significantly speed up timelines, while bringing down costs
341. Hot-Cold Data Separation: How It Cuts Your Storage Costs by 70%
Apparently hot-cold data separation is hot now. Let's figure out why.
342. How to Perform Data Augmentation with Augly Library
Data augmentation is a technique used by practitioners to increase the data by creating modified data from the existing data.
343. Programmers Not Proficient in AIGC Programming May Face Obsolescence in the Next 5 Years
After reading this, you'll likely have a whole new perspective on how AIGC enhances development efficiency.
344. LLM Vulnerabilities: Understanding and Safeguarding Against Malicious Prompt Engineering Techniques
Discover how Large Language Models face prompt manipulation, paving the way for malicious intent, and explore defense strategies against these attacks.
345. Model Calibration in Machine Learning: An Important but Inconspicuous Concept
A prelusive article comprehending the concept behind model calibration, its importance and usage in machine learning model development.
346. 23 Common Data Science Interview Questions for Beginners
In 2012, Harvard Business Review called data scientists the sexiest job of the 21st century. However, correctly answering data science interview questions to get a job as a data scientist is very tricky.
347. Unpredictability of Artificial Intelligence
The young field of AI Safety is still in the process of identifying its challenges and limitations. In this paper, we formally describe one such impossibility result, namely Unpredictability of AI. We prove that it is impossible to precisely and consistently predict what specific actions a smarter-than-human intelligent system will take to achieve its objectives, even if we know terminal goals of the system. In conclusion, impact of Unpredictability on AI Safety is discussed.
348. Influenza Vaccines: The Data Science Behind Them
349. Advancing User Data Governance with Data Lineage
This article will discuss how data lineage can help in user data governance and explore how serverless technology can be incorporated to achieve better results.
350. How to Install the KNIME Analytics Data Science Software
KNIME Analytics is a data science environment written in Java and built on Eclipse. This software allows visual programming for data science applications.
351. On the difficulty of creating a data science code of ethics
352. SeaTunnel CDC Explained: A Layman’s Guide
The core design philosophy of SeaTunnel CDC is to find the perfect balance between "Fast" (parallel snapshots) and "Stable" (data consistency).
353. What Are the Top Startups in Oceania?
At HackerNoon, we pride ourselves on supporting startups because we know how hard it can be to start and run a company.
354. How to Use Data Science to Find the Best Seat in the Cinema (Part I)
From the most popular seats to the most popular viewing times, we wanted to find out more about the movie trends in Singapore . So we created PopcornData — a website to get a glimpse of Singapore’s Movie trends — by scraping data, finding interesting insights, and visualizing them.
355. Top AI and ML YouTube Channels for Data Scientists to Subscribe to
Subscribe to these Machine Learning YouTube channels today for AI, ML, and computer science tutorial videos.
356. BlobGAN: A BIG step for GANs
BlobGAN allows for unreal manipulation of images, made super easily controlling simple blobs. All these small blobs represent an object, and you can move them around or make them bigger, smaller, or even remove them, and it will have the same effect on the object it represents in the image. This is so cool!
357. How to Build a Bar Chart Race on COVID-19 Cases in 5 Minutes
Using the new Tableau version 2020.1 onwards.
358. How this Web3 Project is Unlocking a Trillion-Dollar Data Economy with Data NFTs
Learn why data could become the most promising NFT utility that sets the foundation for a valuable trend: Data Finance (DataFi).
359. 5 Use Cases of AI to Show How It Is Transforming the Industry
Although the internet made a lot of things easier for the insurance companies, there were still many pain points left to be addressed.
360. Using GANs to Create Anime Faces via Pytorch
Most of us in data science have seen a lot of AI-generated people in recent times, whether it be in papers, blogs, or videos. We’ve reached a stage where it’s becoming increasingly difficult to distinguish between actual human faces and faces generated by artificial intelligence. However, with the current available machine learning toolkits, creating these images yourself is not as difficult as you might think.
361. PULSE: Photo Upsampling Makes Blurry Faces 60 Times Sharper
The new PULSE: Photo Upsampling algorithm transforms a blurry image into a high-resolution image.
362. How AI Transforms the Fitness Industry
Incorporating AI into fitness apps can still be confusing, especially if you haven’t worked with AI before. Learn how AI can be applied to the fitness industry.
363. How PostgreSQL Aggregation Inspired Timescale Hyperfunctions’ Design
Get a primer on PostgreSQL aggregation, how PostgreSQL´s implementation inspired us as we built TimescaleDB hyperfunctions and what it means for developers.
364. 👨🔬️ Top 10 Data Scientist Skills to Develop to Get Yourself Hired
List of Top 10 Data Scientist skills that guaranteed employment. As well as a selection of helpful resources to master these skills
365. 7 Open Source Projects Every Data Scientist/Analyst Needs to Bookmark 🚀
Check out these 7 amazing open source projects that every data scientist /analyst should know about. These tools can make your life so much easier.
366. How Uber Uses AI to Improve Deliveries
How can Uber deliver food and always arrive on time or a few minutes before? How do they match riders to drivers so that you can always find a Uber? All that while also managing all the drivers?!
367. 10 Best + Free Machine Learning Courses Collection
Here's a compilation of some of the best + free machine learning courses available online.
368. TikTok: A Ticking Time Bomb?
One of the most popular apps of 2019, TikTok ruled the download charts in both the Android and Apple markets. Having more than 1.5 billion downloads and approximately half a billion monthly active users, TikTok definitely has access to a trove of users. With that large user base comes a hidden goldmine: their data.
369. Proxy Servers for Your Data Science Project: A Comprehensive Guide
A data-driven intro to proxies in the context of web scraping.
370. How to Build Basic Chatbot Without Coding and Deploy to Websites
Build best automated AI chat bot using Google Dialog flow
371. 10 Data Science and Machine Learning Libraries for Python
372. Graphs in the 2020s: Databases, Platforms and The Evolution of Knowledge
Graphs, and knowledge graphs, are key concepts and technologies for the 2020s. What will they look like, and what will they enable going forward?
373. We're collecting AI problem statements to crowdsource solutions to data scientists
As technology penetrates every facet of life, and continues to grow exponentially, the solution potential becomes enormous. At the same time, we're in a world where billions live in poverty, and millions are on the brink of famine. In order to support an ever-growing populace, we need to leave no stone un-turned in the search for solutions. AI provides many potential solutions to humanity's greatest challenges."AI" is a vague, even confusing term. If you hear the phrase "artificial intelligence," you might wonder why there aren't sentient robots walking around, or why everyone isn't in self-driving cars already. The reality is that "AI" is just a marketing term for a set of computational statistical tools, or more simply, algorithms.However, as versatile as mathematics is, so is AI. AI is limited by (primarily) a couple things: data and computational power. Both the data and the compute power we have available are growing exponentially, so AI is becoming more and more powerful.With this increase in data and computational ability, AI is now being used in a wide variety of applications.For example, bitgrit (disclaimer: I'm CEO), collects meaningful AI problem statements to crowd-source solutions to data scientists. Some problem statements include saving animals’ lives, increasing agricultural yield, and speeding up healthcare claims processing.Michael Suttles, CEO at Save All The Pets, explains how data and AI can be used to save shelter animals:
374. A Primer On The AI Economy
Each time a new business ecosystem forms, we have to ask a simple question: where's value created?
375. Harnessing Scalable Vector Graphics (SVG) for Effective Data Visualization
Learn About SVG for Data Visualization, to make Complex Information Clear and Beautiful.
376. 70-Page Report on the COCO Dataset and Object Detection [Part 2]
This blog is part 1 of (and contains a link to) a 70+ page report was created to quickly find data resources and/or assets for a given dataset and a specific ta
377. A JavaScript Infographic: Data Science Salaries in 2022
Data visualisation infographic with insights on salary level of data scientists - how to create the JavaScript dashboard and analyse its data
378. Integrate AI into Data Mapping to Drive Business Decision Making
Prior to analyzing large chunks of data, enterprises must homogenize them in a way that makes them available and accessible to decision-makers. Presently, data comes from many sources, and every particular source can define similar data points in different ways. Say for example, the state field in a source system may exhibit “Illinois” but the destination keeps it is as “IL”.
379. How I Broke Into Data Science
A software engineer’s journey into data science at Yelp and Uber
380. #Mythbusting 10 Artificial Intelligence Misconceptions
Today, misconceptions about AI are spreading like wildfire.
381. Leveraging Data Science in eCommerce: 7 Projects to Try
As an online retailer, how can you improve your business? Of course through providing a better customer experience. An e-commerce company needs to have a well understanding of the following factors:
382. MIDAS: A State-of-the-Art Model for Anomaly Detection in Graphs
In machine learning, hot topics such as autonomous vehicles, GANs, and face recognition often take up most of the media spotlight. However, another equally important issue that data scientists are working to solve is anomaly detection. From network security to financial fraud, anomaly detection helps protect businesses, individuals, and online communities. To help improve anomaly detection, researchers have developed a new approach called MIDAS.
383. Interviews with My Machine Learning Heroes
Meta Article with links to all the interviews with my Machine Learning Heroes: Practitioners, Researchers and Kagglers.
384. Reflinks vs symlinks vs hard links, and how they can help machine learning projects
Hard links and symbolic links have been available since time immemorial, and we use them all the time without even thinking about it. In machine learning projects they can help us, when setting up new experiments, to rearrange data files quickly and efficiently in machine learning projects. However, with traditional links, we run the risk of polluting the data files with erroneous edits. In this blog post we’ll go over the details of using links, some cool new stuff in modern file systems (reflinks), and an example of how DVC (Data Version Control, https://dvc.org/) leverages this.
385. LLM-Powered OLAP: the Tencent Experience with Apache Doris
Adopting AI in our data analytic solution is a bumpy journey, but phew, it now works well for us.
386. Be a Shortstop Beagle: Learn How to Update R and RStudio to the Latest Version
Learn how to update your Rstudio open source software and why you should keep it up to date.
387. Analyzing Data From U.S. Road Accidents With Data Visualization
In this article, we would be analyzing data related to US road accidents, which can be utilized to study accident-prone locations and influential factors.
388. Rio: WebApps in Pure Python - No JavaScript, HTML, or CSS Needed!
Rio is a brand new GUI framework designed to let you create modern web apps with just a few lines of Python. Our goal is to simplify web and app development.
389. Why So Many AI Initiatives Fail
Why is it that, for most organizations, building successful AI applications is a huge challenge? It can be boiled down to three big hurdles.
390. Why Data Anomalies are More Important Than You Think
It is easy to be annoyed by strange anomalies when they are sighted within otherwise clean (or perhaps not-quite-so-clean) datasets. This annoyance is immediately followed by eagerness to filter them out and move on. Even though having clean, well-curated datasets is an important step in the process of creating robust models, one should resist the urge to purge all anomalies immediately — in doing so, there is a real risk of throwing away valuable insights that could lead to significant improvements in your models, products, or even business processes.
391. Regression Analysis on Life Expectancy
392. 10 Patterns of Centralized Crypto Exchanges Explained Using Machine Learning and Data Visualizations
Centralized crypto exchanges are the most important black box of the crypto ecosystem. We all use them, we have a love-hate relationship with them, and we understand very little about their internal behavior. At IntoTheBlock, we have been heads down working on a series of machine learning models that help us better understand the internal of crypto exchanges. Recently, we presented some of our initial findings at a highly oversubscribed webinar and I thought it would be elaborate further in some of the ideas discussed there.
393. ML Essentials: Top 10 Lists Every Data Scientist Should Know
Data Science is no doubt the "sexiest" career path of the 21st century, made up of people with strong intellectual curiosity and technical expertise to dig out valuable insights from humongous volumes of data. This helps firms add value by improving their productivity, unlocking insights for better decision making and profit gains, just to mention a few. The knowledge of Data Science is desirable and useful across various industries.
394. HDTree: A Customizable and Interactable Decision Tree Written in Python
Introducing a customizable and interactable Decision Tree-Framework written in Python
395. 8 Companies Using Machine Learning in Cool Ways
When asked what advice he'd give to world leaders, Elon Musk replied, "Implement a protocol to control the development of Artificial Intelligence."
396. Programming From 8 To 80
Is there a programming language that's good for every user from age 8 to 80? You bet! It's called Smalltalk.
397. LegalTech - An Overview and Prospective Future
A primer to understand how technology is poised to disrupt law
398. 6 important Python Libraries for Machine Learning and Data Science
In this guide, we’ll show the must know Python libraries for machine learning and data science.
399. Machine Learning News Roundup - 6 Essential AI Articles of 2019
400. What Kind of Scientist Are You?
Data science came a long way from the early days of Knowledge Discovery in Databases (KDD) and Very Large Data Bases (VLDB) conferences.
401. From Time Series to Causal Scenarios: A Statistical Guide to Counterfactual Forecasting
Learn how counterfactual forecasting helps data scientists measure true revenue impact by simulating causal scenarios beyond traditional time series models.
402. Building a Propensity Model to Target Users Better in Marketing Campaigns
Propensity model to figure out the likelihood of a person buying a product on their return visit.
We need to identify the probability to convert for each user.
403. WTF is Automatic Speech Recognition?
404. Introduction 5 Different Types of Text Annotation in NLP
Natural language processing (NLP) is one of the biggest fields of AI development. Numerous NLP solutions like chatbots, automatic speech recognition, and sentiment analysis programs can improve efficiency and productivity in various businesses around the world.
405. Takeaways And Quotes From The World’s Largest Kaggle GrandMaster Panel
406. 15 Essential Python Libraries for Data Science and Machine Learning
Discover 15 essential Python libraries for data science & machine learning, covering data mining, visualization & processing.
407. Building a Point Map in JavaScript
Master creating interactive point maps in JavaScript! Step-by-step guide using millionaire counts for global cities for illustration. Dive in now!
408. A Step-by-Step Guide to Failing a Data Science Project
As posited by Lev Tolstoy in his seminal work, Anna Karenina: “Happy families are all alike; every unhappy family is unhappy in its own way.” Likewise, all successful data science projects go through a very similar building process, while there are tons of different ways to fail a data science project. However, I’ve decided to prepare a detailed guide aimed at data scientists who want to make sure that their project will be a 100% disaster.
409. 6 Essential Tips to Solve Data Science Projects
Data science projects are focusing on solving social or business problems by using data. Solving data science projects can be a very challenging task for beginners in this field. You will need to have a different skills set depending on the type of data problem you want to solve.
410. COVID-19: Perceived Spread vs. True Spread in China, Italy and the US
Here at TimeNet, we’re building a large time series database with the primary aim of benefitting society through access to data. In this post we’ll study different time series representing both the true, and the perceived spread of the coronavirus (COVID-19) pandemic. Daily COVID-19 numbers are currently available on TimeNet.cloud for many countries. We’re expanding these datasets with further variables measuring how we (people) perceive the significance of the pandemic. We use stock market movements and internet search trends to quantify the virus’s perceived spread.
411. A Complete Guide To The Machine Learning Tools On AWS
In this article, we will take a look at each one of the machine learning tools offered by AWS and understand the type of problems they try to solve for their customers.
412. The One Data Science Project Idea That’ll Impress Interviewers
Let’s talk about the one and only project you need to build, that’ll help you gain fullstack data science experience, and impress interviewers on your interviews if your goal is to jumpstart your career in data science.
413. Data Science Teams are Doing it Wrong: Putting Technology Ahead of People
Data Science and ML have become competitive differentiator for organizations across industries. But a large number of ML models fail to go into production. Why?
414. Migrate Data from S3 to Snowball
In this article, I will show you how to migrate data from S3 to Snowball.
415. 10 Free Resources to Become a Health Data Scientist
Becoming a health data scientist can be challenging but rewarding; it merges statistical analysis with other tools to gain insights from healthcare data.
416. Beyond Artificial Intelligence: Providing Insights to Your Customers
<meta name="monetization" content="$ilp.uphold.com/EXa8i9DQ32qy">
417. Can You Open Medical Data (MR, CT, X-Ray) in Python and Find Tumors With AI?! Maybe
How to access medical data in DICOM format (MR, CT, X-Ray) from Python
418. Top 6 CI/CD Practices for End-to-End Development Pipelines
Maximizing efficiency is about knowing how the data science puzzles fit together and then executing them.
419. 20 Data Science Podcasts You Don’t Want to Miss
Podcasts have unequivocally become one of the most dominant forms of media consumption in recent years.
420. How to Seamlessly Transfer From Airflow to Apache DolphinScheduler With Air2phin
Some users need to migrate their scheduling system from Airflow to Apache Dolphinscheduler, this is a guide to transfer from Airflow to DolphinScheduler.
421. Meet The Entrepreneur: Alon Lev, CEO, Qwak
Meet The Entrepreneur: Alon Lev, CEO, Qwak
422. Every Way Natural Language is Better Than SQL
Since the dawn of time, humans have communicated through gestures, drawings, smoke, or speech. Along the way, Structured Query Language (SQL) made its way into human life so we could speak to databases. However, it’s time to revert back to our natural language and rethink how we talk to our data.
423. 10 Best African Language Datasets for Data Science Projects
A list of African language datasets from across the web that can be used in numerous NLP tasks.
424. Data Preprocessing
At the heart of Machine Learning is to process data. Your machine learning tools are as good as the quality of your data. This blog deals with the various steps of cleaning data. Your data needs to go through a few steps before it is could be used for making predictions.
425. How to Manage Machine Learning Products [ Part II]
Best practices and things I’ve learned along the way.
426. The Ethical AI Libraries that are Critical for Every Data Scientist to Know
With the exponential rise in applications of AI, Data Science, and Machine Learning these are the critical Ethical AI Libraries to know.
427. Certify Your Data Assets to Avoid Treating Your Data Engineers Like Catalogs
Data trust starts and ends with communication. Here’s how best-in-class data teams are certifying tables as approved for use across their organization.
428. Introducing the Swahili News Dataset for Topic Classification
Swahili (also known as Kiswahili) is one of the most spoken languages in Africa. It is spoken by 100–150 million people across East Africa. Swahili is popularly used as a second language by people across the African continent and taught in schools and universities. In Tanzania, it is one of two national languages (the other is English).
429. Why Learning PyTorch Can Make you a Better Engineer
Pytorch is a powerful open-source deep-learning framework that is quickly gaining popularity among researchers and developers
430. Model.fit is More Complex Than it Looks
Deep dive into why model.fit beats naive normal equations in linear regression, with SVD, conditioning and floating-point pitfalls explained.
431. How Data Teams Can Benefit From Running Like a Product Team
Product teams have a lot of great practices that data teams would benefit from adopting. Namely: user-centricity and proactivity.
432. Using Machine Learning to Recommend Investments in P2P Lending
Introducing PeerVest: A free ML app to help you pick the best loan pool on a risk-reward basis
433. What is a Support Vector Machine?
SVM works by finding a hyperplane in an N-dimensional space (N number of features) which fits to the multidimensional data while considering a margin.
434. Replacing Apache Hive, Elasticsearch and PostgreSQL with Apache Doris
Simplicity is the best policy.
435. Twitter Sentiment Analysis for the 2019 Lok Sabha Elections
Introduction
436. A Pleasant Way to Kick Off Your Data Science Education- This is CS50
So You Want to Get Into Data Science
437. Apache Cassandra: The Database that Helps Uber and Apple De-risk Their AI Projects
Large-scale users of Cassandra, like Uber and Apple, exemplify how this database system can effectively lower the risk in AI/ML projects.
438. Will Generative Models Be The Next Machine Learning Boom?
Machine Learning is a rapidly growing and very complex field of study. Generative Models might prove to be a new breakthrough that will make a new boom.
439. How to Build the Perfect CV to Land a Data Science Role
Looking to make your data scientist resume more attractive to employers?
440. Difference Between Boosting Trees: Updates to Classics With CatBoost, XGBoost and LightGBM
Explore boosting trees' evolution: from AdaBoost to XGBoost, LightGBM, and CatBoost. Learn key updates & how to choose the right library for your needs.
441. The Usefulness Of Data Science In Law Enforcement
Law enforcement agencies are not new to the data and its usage, but with the advancement in technology, Data science in law enforcement has become a need.
442. An Introduction to Automation in Vision AI
Levels of Annotation Automation
443. Top 5 Machine Learning Platforms to Watch in 2022
Machine Learning Operations (MLOps) is a form of DevOps in a growing area. In this article, we'll discuss the top 5 Machine Learning Platforms to watch in 2022.
444. PSG is a New Task for AIs Requiring Higher Levels of Understanding
Panoptic scene graph generation, or PSG, is a new problem task aiming to generate a more comprehensive graph representation of an image or scene based on panoptic segmentation rather than bounding boxes. It can be used to understand images and generate sentences describing what's happening. This may be the most challenging task for an AI! Learn more in the video...
445. Best Machine Learning Books You Should Read: 2020 Edition
These books cover the Introductory level to Expert level of knowledge and concepts in ML. These Books have some core factors about ML. Give them a try. Lets Start.
446. LLMs Are Transforming AI Apps: Here's How
Building apps with unreal levels of personalized context has become a reality for anyone who has the right database, a few lines of code, and an LLM like GPT-4.
447. 7 Real-World Applications of AI in Healthcare
448. Why Are Databases Exposed as APIs?
What has changed in the software world to elevate the importance of exposing databases as APIs?
449. Hadoop Data Storage Explained
Explore how exactly distributed storage works in Hadoop? We have to characterize an essential node (known as NameNode) from one of the workers (DataNodes).
450. Top 25 Quotes from ML Heroes Interviews (+ an exciting announcement!)
Re-boot of “Interview with Machine Learning Heroes” and collection of best pieces of advice
451. An Old Statistical Trick Might Help Better Explain the Apparent Correlation Between Bitcoin and Gold
The relationship between Bitcoin and Gold is one of the dynamics that seems to constantly capture the minds of financial analysts. Recently, there have been a series of new articles claiming an increasing “correlation” between Bitcoin and Gold and the phenomenon seems to be constantly debated in financial media outlets like CNBC or Bloomberg.
452. Use the 80/20 Rule with Moderation
453. An Interview with Google Map's Time Prediction Algorithm Creator
An interview with Petar Veličković, research scientist at Google Deepmind, The What's AI Podcast episode 17!
454. How to Get Started with Data Version Control (DVC)
Data Version Control (DVC) is a data-focused version of Git. In fact, it’s almost exactly like Git in terms of features and workflows associated with it.
455. My First Experience Deploying an ML Model to Production
In this blog, I will be sharing my learnings and experience from one of the deployed models.
456. Top 10 On-Demand IT Certifications With Highest Pay: 2020 Edition
Information Technology (IT) certification can enrich your IT career and pave the way for a profitable way. As the demand for IT professionals increases, let's look at 10 high-paying certifications. The technology landscape is constantly changing and the demand for information technology certification is also getting higher. Popular areas of IT include networking, cloud computing, project management, and security. Eighty percent of IT professionals say certification is useful for careers and the challenge is to identify areas of interest. Let's take a look at the certifications that are most needed and the salaries that correspond to them.
457. A Beginner Guide to Incorporating Tabular Data via HuggingFace Transformers
Transformer-based models are a game-changer when it comes to using unstructured text data. As of September 2020, the top-performing models in the General Language Understanding Evaluation (GLUE) benchmark are all BERT transformer-based models. At Georgian, we often encounter scenarios where we have supporting tabular feature information and unstructured text data. We found that by using the tabular data in these models, we could further improve performance, so we set out to build a toolkit that makes it easier for others to do the same.
458. 10 Key Skills Every Data Engineer Needs
Bridging the gap between Application Developers and Data Scientists, the demand for Data Engineers rose up to 50% in 2020, especially due to increase in investments in AI-based SaaS products.
459. What is Auditability for AI Systems?
Up until recently, we accepted the “black box” narrative surrounding AI as a necessary evil that could not be extrapolated away from AI as a concept.
460. 🏆 Startups of The Year: 2 Months Left to Cast Your Vote!
HackerNoon's Startups of The Year voting ends March 31, 2025. You still have time to support your favorites—cast your vote today!
461. Optical Character Recognition Technology for Business Owners
How to use Machine learning, Deep learning and Computer Vision for building Optical Character Recognition (OCR) solution for text recognition.
462. Implementing the Weighted Random Algorithm with JavaScript
The Weighted Random algorithm is used to send HTTP requests to Nginx servers. In this article, you'll learn how the Weighted Random algorithm works.
463. Scikit Learn 1.0: New Features in Python Machine Learning Library
Scikit-learn is the most popular open-source and free python machine learning library for Data scientists and Machine learning practitioners. The scikit-learn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction.
464. How I Analyzed One Million Voter Records in Manhattan
What if you could instantly visualize the political affiliation of an entire city, down to every single apartment and human registered to vote? Somewhat surprisingly, the City of New York made this a reality in early 2019, when the NYC Board of Elections decided to release 4.6 million voter records online, as reported by the New York Times. These records included full name, home address, political affiliation, and whether you have registered in the past 2 years. The reason according to this article was:
465. Decoding MySQL EXPLAIN Query Results for Better Performance
Understanding MySQL explains query output is essential to optimize the query. EXPLAIN is good tool to analyze your query.
466. Why I'm in love with Julia
In this article I'm going to make a case why people serious about creating machine learning algorithms and high performance data science programming should use Julia rather than Python.
467. What Apple And Spotify Know About Me
Unsurprisingly, the data that our apps have collected about us is both impressive and concerning, though it can be very interesting to review and explore it.
468. How To Productionalize ML By Development Of Pipelines Since The Beginning
Writing ML code as pipelines from the get-go reduces technical debt and increases velocity of getting ML in production.
469. A Product Manager's 600-Word Guide to Machine Learning
Machine learning (ML) is a technology or field of computer science that learns from historical data to make accurate predictions or decisions.
470. Data Science Training and Data Science - Machine Learning With Python
The requirement for its stockpiling also grew as the world entered the period of huge information. The principle focal point of endeavors was on structure framework and answers for store information. When frameworks like Hadoop tackled the issue of capacity, preparing of this information turned into a challenge. Data science began assuming a crucial job to take care of this issue. Information Science is the fate of Artificial Intelligence as It can increase the value of your business.
471. Data Engineering Tools for Geospatial Data
Location-based information makes the field of geospatial analytics so popular today. Collecting useful data requires some unique tools covered in this blog.
472. What If Your LLM Is a Graph? Researchers Reimagine the AI Stack
The global knowledge graph market is projected to reach $6.93 billion by 2030.
473. Will AI Take Your Job? The Data Tells a Very Different Story
Historically, technological revolutions have triggered similar waves of anxiety, only for the long-term outcomes to demonstrate a more optimistic narrative.
474. 3 Easy Ways to Improve The Performance Of Your Python Code
I. Benchmark, benchmark, benchmark
475. How to Achieve Optimal Business Results with Public Web Data
Public web data unlocks many opportunities for businesses that can harness it. Here’s how to prepare for working with this type of data.
476. Foundation Series: Data Science, Psychohistory, and the Future of Humanity
A world where the future of humanity can be predicted through an interdisciplinary science called psychohistory! A data scientist's review of Foundation Series.
477. How To Implement 3D Human Pose Estimation System In AI Fitness Apps
“Is it possible for a technology solution to replace fitness coaches? Well, someone still has to motivate you saying “Come On, even my grandma can do better!” But from a technology point of view, this high-level requirement led us to 3D human pose estimation technology.
478. AutoScraper and Flask: Create an API From Any Website in Less Than 5 Minutes
In this tutorial, we are going to create our own e-commerce search API with support for both eBay and Etsy without using any external APIs.
479. 4 Tips To Become A Successful Entry-Level Data Analyst
Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand.
480. 8 Skills Required To Become A Data Scientist
Back in 2016, Glassdoor declared that being a Data Scientist was the best job in America.
481. LLMs For Curating Your Social Media Feeds? Yes Please!
Our online feeds are broken and large language models have the power to fix them.
482. Graph Algorithms, Neural Networks, and Graph Databases
Year of the Graph Newsletter, September 2019
483. How to Get Started with Data Governance Best Practices
Long recognized as a must in the data-driven world, data governance has never been easy for big and tiny organizations alike.
484. Understanding The Concept of Clustering In Unsupervised Learning
What is hierarchical clustering in unsupervised learning?
485. What I Learned When I Changed the UX Research System at my Company
SUS scale and why you should try to use it in your UX research.
486. Building with PHP in the Era of Microservices and API-Driven Architectures
Microservices can be made using lightweight PHP frameworks like Slim or Lumen, providing minimalistic setups that allow developers focus on core functionalities
487. How BitMEX Wallets Impact the Price of Bitcoin
Part of building a profitable trading strategy is quickly testing novel ideas. These tend to be the money makers in the rare case that they prove useful once you can integrate them into your strategy.
488. The Ultimate Guide to Strategic Thinking for Data Scientists
While HBR declared "Data Scientist" the sexiest job of the 21st century, let's admit that the prevailing view is that it's a geeky, highly-technical field.
489. Top 5 AI Articles of February 2022 Every Data Scientist Should Read
Here are the five best articles related to artificial intelligence in February, hoping they will make you want to learn more and visit their website.
490. How To Create a Useful Educational Product for Adults using Motivational Design
The main metric for educational product is it's completition rate. To improve it, one can use the principles of motivational design.
491. A Complete(ish) Guide to Python Tools You Can Use To Analyse Text Data
Exploratory data analysis is one of the most important parts of any machine learning workflow and Natural Language Processing is no different.
492. 5 Papers on Face Recognition Every Data Scientist Should Read
Facial recognition, is one of the largest areas of research within computer vision. This article will introduce 5 face recognition papers for data scientists.
493. Accessing Real-time Smart Contract Data From Python Code (Using Lido Contract as an Example)
In this tutorial, I’ll show you more sophisticated data access tools that are more like a surgical scalpel.
494. Top 13 Data Visualization Tools for 2023 and Beyond
With the enormity of data, data visualization has become the most sought-after method to depict huge numbers in simpler versions of maps or graphs.
495. The Bitcoin Mempool: Where Transactions Take Flight
One of Bitcoin’s strengths and the thing that makes it unique in the finance world is its radical transparency. Blockchain data is like a window, you can see right through it.
496. Some Shocking Data Analyses About Stablecoins
Stablecoins are one of the most relevant developments in the crypto ecosystem and one that has been increasingly getting traction. Recently, I presented a session that highlighted some interesting analyses that arise from applying data science methods on stablecoin’s blockchain data. The slide deck and video from the session will be available soon but I thought I share some of the most intriguing data points.
497. Are Traditional Data Warehouses Being Devoured by Agentic AI?
From a technical architecture perspective, I believe this wave of AI will profoundly reshape the entire software ecosystem.
498. A/B Testing was a Jerk, Until we Found the Replacement for Druid
The recipe for successful A/B testing is quick computation, no duplication, and no data loss. So, we used Apache Flink and Doris to build our data platform.
499. Want to Create Data Circuit Breakers with Airflow? Here's How!
See how to leverage the Airflow ShortCircuitOperator to create data circuit breakers to prevent bad data from reaching your data pipelines.
500. How to Build Machine Learning Algorithms that Actually Work
Applying machine learning models at scale in production can be hard. Here's the four biggest challenges data teams face and how to solve them.
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