Let's learn about Machine Learning Tutorials via these 87 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.
Machine learning tutorials are educational resources that guide users through the concepts and practical applications of machine learning. They matter for democratizing AI knowledge, enabling individuals to build and understand intelligent systems that drive innovation.
1. 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.
2. 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.
3. Intro to Audio Analysis: Recognizing Sounds Using Machine Learning
4. How To Use Microsoft Excel To Classify Your Data
An accessible introduction to ML - no programming or math required. By the end of this tutorial, you’ll have implemented your first algorithm without touching a single line of code. You’ll use Machine Learning techniques to classify real data using basic functions in Excel. You don’t have to be a genius or a programmer to understand machine learning. Despite the popularized applications of self-driving cars, killer robots, and facial recognition, the foundations of machine learning (ML) are quite simple. This is a chance to get your feet wet and understand the power of these new techniques.
5. Golang in Machine Learning
Can Golang be used in Machine Learning? In the article you will learn advantages and disadvantages of using Go lang in Machine learning
6. An Essential Python Text-to-Speech Tutorial Using the pyttsx3 Library
Basically, what we want to do is to give some piece of text to our program and it will convert that text into the speech and will read that to us.
7. NLP Tutorial: Creating Question Answering System using BERT + SQuAD on Colab TPU
8. 9 Reasons Why You Should Keep Learning Machine Learning
Machine Learning is an application of Artificial Intelligence. It allows software applications to become accurate in predicting outcomes. Machine Learning focuses on the development of computer programs, and the primary aim is to allow computers to learn automatically without human intervention.
9. Deploy Computer Vision Models with Triton Inference Server
There are a lot of Machine Learning courses, and we are pretty good at modeling and improving our accuracy or other metrics.
10. 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
11. The Full Story behind Convolutional Neural Networks and the Math Behind it
Convolutional Neural Networks became really popular after 2010 because they outperformed any other network architecture on visual data, but the concept behind CNN is not new. In fact, it is very much inspired by the human visual system. In this article, I aim to explain in very details how researchers came up with the idea of CNN, how they are structured, how the math behind them works and what techniques are applied to improve their performance.
12. Machine Learning Model with FLASK REST API
In this tutorial we will see how you can make your first REST API for Machine Learning Model using FLASK. We will start by creating machine learning model. Then we will see step-by-step procedure to create API using Flask and test it using Postman.
13. [Hacking Tinder] Train an AI to Auto-Swipe for You 🖖
Auto-tinder was created to train an AI using Tensorflow and Python3 that learns your interests in the other sex and automatically plays the tinder swiping-game for you.
14. 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.
15. 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.
16. 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.
17. The Most Detailed Guide On MLOps: Part 1
This MLOps guide discusses what is MLOps, MLOps definition, MLOps maturity levels, MLOps conceptual framework, MLOps core processes, automated ML workflow, etc.
18. Build a Monster-Finding Tool For Your Next D&D Session That Picks the Right Encounter For You
As Dungeon Master, you craft epic encounters—but finding the perfect D&D monster is tough. Let’s build a tool that picks the ideal foe with vector search magic!
19. How To Compare Documents Similarity using Python and NLP Techniques
In this post we are going to build a web application which will compare the similarity between two documents. We will learn the very basics of natural language processing (NLP) which is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language.
20. 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.
21. How I Approached Machine Learning Interviews at FAANGs as an ML Engineer
Cracking a Machine learning interview at companies like Facebook, Google, Netflix, Snap etc. really comes down to nailing few patterns that FAANGs look for.
22. Build a GUI for Your Machine Learning Models
How to build a cool GUI for your Machine Learning models with Gradio so that you can visualise your models easily and effectively for people to understand.
23. 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.
24. 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?
25. 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.
26. Adversarial Examples In Machine Learning Explained
There are easy ways to build adversarial examples that can fool any deep learning model and create security issues no matter how complex the model is.
27. [Tutorial] Build a Gender Classifier for Live Webcam Stream using Tensorflow and OpenCV
Training a Neural Network from scratch suffers two main problems. First, a very large, classified input dataset is needed so that the Neural Network can learn the different features it needs for the classification.
28. Image Style Transfer And Video Transformation In EbSynth
29. How to Leverage Machine Learning to Improve AdWords Efficiency
Recent issues surrounding racial inequality in the United States have led to direct action in the digital marketing world as well. More and more companies are pausing their Facebook ad campaigns because of the social network’s inaction on discrimination and hate speech.
30. Waiting for your A/B Testing Results — Guide for Easy Acceleration
Explore techniques for accelerating A/B testing, including paired testing, covariance adjustment, stratification, CUPED, CUPAC, and Bayesian approaches.
31. Train ML Models in Docker Container and Configure Access to on-premise GPU device
Train ML models in Docker container and configure access to on-premise GPU device
32. 10 Best + Free Machine Learning Courses Collection
Here's a compilation of some of the best + free machine learning courses available online.
33. 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
34. Deploying Transformers in Production: Simpler Than You Think
A beginner-friendly guide showing developers how to easily deploy transformer models (like DistilBERT) using Docker, Flask, Gunicorn, and AWS SageMaker. Include
35. Machine Learning: Your Ultimate Feature Selection Guide Part 2 - Select the Real Best
Explore ML feature selection: Dive into wrapper and embedded methods for optimized machine learning models in Part 2 of our series.
36. 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.
37. The Four Types of Machine Learning | Part 2
In the previous post, we saw the first two types of machine learning. In this post, we will discuss the other two types of machine learning. These are — Semi-su
38. From TF to TFLite: Deploying ML Models on Mobile [Part 1]
tl;dr - Link to code: TensorFlow GAN model.
So the other day I was talking to my rubber ducky about how G-Board predicts my next word, even when those words are entirely made up by me, in that how it actually learns on-device. How amazingly Netflix, Amazon, Google Maps make use of machine learning in their apps. How does machine learning on apps even work? Does the model learn even after being deployed? Can I deploy a GAN model on mobile?
39. Kinetics Dataset - Training and Evaluating Models for Video Classification
A guide to using the open-source tool FiftyOne to download the Kinetics dataset and evaluate video understanding models
40. 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.
41. How I built this: Machine learning with Amazon Personalize and a Customer Data Platform
Learn how an infrastructural Customer Data Platform can help you overcome common machine learning challenges with this use case tutorial.
42. Top 8 Machine Learning Content Creators on YouTube
Here are the top Machine Learning content creators on YouTube to follow for tutorials, deep learning, and more.
43. 70-Page Report on the COCO Dataset and Object Detection [Part 3]
44. Building a Monte Carlo Markov Chain Pipeline Using Luigi
A few months ago I was accepted into a data science bootcamp - Springboard, for their data science career track. As part of this bootcamp I had to work on Capstone projects that would help build my portfolio, show my ability to extract, clean up data, build models and extract insights from said models. For my first project I opted to build a Monte Carlo Markov Chain pipeline initially with the objective of building a multi-touch attribution model that would help me understand conversion rates from different states in the signup process and use that to understand which channels appeared to deliver the greatest conversion rates for users coming through a given landing page and transitioning through the different signup states defined in my dataset.
45. How to Build a Sentiment Analysis App Using Gradio and Hugging Face
In this article, we are going to create an end-to-end AI Sentiment Analysis web application using Gradio and hugging face transformers.
46. [Explained] Machine Learning Fundamentals: Optimization Problems and How to Solve Them
If you start to look into machine learning and the math behind it, you will quickly notice that everything comes down to an optimization problem. Even the training of neural networks is basically just finding the optimal parameter configuration for a really high dimensional function.
47. How to Train Computer Vision Models Efficiently
The starting point of building a successful computer vision application is the model. Computer vision model training can be time-consuming and challenging if one doesn’t have a background in data science. Nonetheless, it is a requirement for customized applications.
48. Top 12 Javascript Libraries for Machine Learning
Rapidly evolving technologies like Machine Learning, Artificial Intelligence, and Data Science were undoubtedly among the most booming technologies of this decade. The s specifically focusses on Machine Learning which, in general, helped improve productivity across several sectors of the industry by more than 40%. It is a no-brainer that Machine Learning jobs are among the most sought-after jobs in the industry.
49. Build A Commission-Free Algo Trading Bot By Machine Learning Quarterly Earnings Reports [Full Guide]
Introduction
50. Machine Learning Made Simple: A Beginner's Guide to AI
From breaking down complex jargon to exploring real-world applications and ethical considerations, this article makes AI approachable for everyone.
51. The Four Types Of Machine Learning
There are three types of machine learning. Initially, there were three, but later type added one more type to the ranks of machine learning types. Thus in total
52. Artificial Intelligence (AI) VS Machine Learning (ML) - A Beginner's Guide
If you are new to the AI and ML world, this guide is for you to clear the doubts between both domains.
53. Logistic Regression: Train Model In Python And Use It on Angular Front End
Demo for this article can be found here.
54. Encoding Categorical Data for ML Algorithms
Encoding is a technique used to convert categorical data to numerical representations to be able to use the data in machine learning algorithms.
55. Scikit-Learn 0.24: Top 5 New Features
For any data scientists & machine-learning engineers use scikit-learn for different machine learning projects here are 5 best new features in scikit-learn 0.24
56. Why Data Science Competitions are Important & How to Get Started
To become a Data Scientist, you have to learn, gain the required skills and practice a lot to get more experience. Participating in data science competitions has been one of the best approaches to help beginners in data science get more experience and finally apply for job opportunities.
57. Fine-Tuning Machine Learning Models with DVC Experiments for Transfer Learning
You can work with pretrained models and fine-tune them with DVC experiments.
58. Loan Risk Prediction Using Neural Networks
A Step-by-Step Guide (With a Healthy Dose of Data Cleaning)
59. Decoding AI: Dive Deep Into Neural Networks and Create Your Own from Scratch!
The journey into the world of artificial intelligence and machine learning is not just about learning about technology.
60. How to Build a Text Summarizer with Gradio and Hugging Face Transformers
Craft concise summaries like a pro: Build your text summarizer web app with Gradio and NLP magic.
61. Machine Learning: Your Ultimate Feature Selection Guide Part 1 - Filter the Most
Explore key feature selection methods in machine learning, focusing on cost-effective filter methods for optimizing model performance. Stay tuned for more!
62. Mastering K-Means: Data Clustering Simplified
In the vast landscape of data analysis and machine learning, uncovering meaningful patterns hidden within datasets is often akin to discovering buried treasures
63. 70-Page Report on the COCO Dataset and Object Detection [Part 1]
Quickly find common resources and/or assets for a given dataset and a specific task, in this case dataset=COCO, task=object detection
64. The Most Detailed Guide On MLOps: Part 2
Not all market players consciously approach the choice of hardware for the ML-system. This is especially noticeable in terms of GPU selection.
65. Curious About Faster ML Models? Discover Model Quantization With PyTorch!
Static quantization tutorial using Pytorch to speed up inference by as much as 4x!
66. The Hunt for Data: Creating a Computer Vision Dataset for Road Safety
In this article, I would like to share my own experience of developing a smart camera for cyclists with an advanced computer vision algorithm
67. Evaluating Regression Models in Machine Learning
Model evaluation is very important since we need to understand how well our model is performing.
68. Software Engineering Best Practices Collection for Machine Learning
69. The Hundred-Page Machine Learning Book [Review]
I first ordered The Hundred-Page Machine Learning book back in May and am only just now finishing it up. In COVID-time, that was about 10 years ago. As you might have inferred, this book is NOT a quick read. What it lacks in easy reading, it makes up for in efficiency. This book swallows up the heavyweight mathematics textbooks and spits out a slim product no thicker than the width of my smartphone. From page one all the way to page 136, Andriy Burkov, the author, does not waste a single word in distilling the most practical concepts in machine learning. You read that right. It is MORE than 100 pages! Sounds like the book has some bias. Get it? Now get ready for my hundred-page book review. Just kidding.
70. What are Decision Trees in Machine Learning?
Learn to measure the performance of your Regression Models - Tutorial by Berk Hakbilen
71. Why and How do We Split the Dataset
Dataset is one important part of the machine learning project. Without data, machine learning is just the machine, and learning is stripped from the title. Whic
72. Machine Learning Explained in 5 Minutes
Google uses it to provide millions of search results every hour. It helps Facebook guess your next love interest. Even Elon Musk’s Tesla uses it to make self-dr
73. How Machines Learn Emotions: Sentiment Analysis of Amazon Product Reviews
How do you train machines to identify emotions? This is a tutorial for sentiment analysis of Amazon product reviews using machine learning algorithms.
74. Linear Regression Explained With Sklearn
Master simple & multiple linear regression with sklearn, backed by essential mathematical concepts.
75. Mastering Logistic Regression: A Comprehensive Guide with Practical Example
Unlock the power of Logistic Regression with this comprehensive guide. From understanding the fundamentals to practical implementation.
76. How I Transfer an Artistic Style to Any Image
Machine learning and artificial intelligence have been on my radar for years now, but more as a concept and “thing I should know about.” I didn’t feel that I had the free time or skills to dig into it. However, my attitude about machine learning has changed in the past few months. I have seen new and easier tools become accessible to the public. In this post I will walk you through how to transfer an art style to any image using some of these tools.
77. Top 20 ML Stories For Data Science
Data Science is undoubtedly one of the main fields that every AI, ML, or data science enthusiast crosses paths with. Now with the advancement of data science, it is not just restricted to refine the data and then put it on the board. It is combined with Machine Learning that makes your machines smart by using the data that you just optimized to feed the machine.
78. 64 Stories To Learn About Machine Learning Tutorials
Learn everything you need to know about Machine Learning Tutorials via these 64 free HackerNoon stories.
79. Understanding MCP by Building One: A Beginner's Guide to Creating Your First AI Tool
Learn the Model Context Protocol hands-on by building a simple Story Manager that Claude can interact with no prior experience needed
80. How to Build a Secure Anonymous Feedback System With Django, Twilio, and Pinata
Build a secure anonymous feedback system using Django, Twilio for SMS, Pinata for media uploads, and TailwindCSS for responsive UI. Ensures privacy and secure s
81. The Rise of Credibility Without Verification
This article introduces synthetic ethos: how AI simulates credibility without sources, reshaping authority in law, health, and education through fluent code.
82. How to Understand Your Data in Real-Time Using bytewax and ydata-profiling
A hands-on tutorial on how to combine the open-source streaming solution, bytewax, with ydata-profiling, to improve the quality of your streaming flows.
83. Building Machine Learning Models With TensorFlow
In this article, I will share with you some useful tips and guidelines that you can use to better build better deep learning models.
84. The Machine Learning Stack Is Being Rebuilt From Scratch Here's What Developers Need to Know in 2026
From foundation models to agentic pipelines - 6 machine learning trends developers must understand to build reliable AI systems in 2026.
85. The Future is Visual: The Image Search Revolution
Images surround us everywhere. Traditionally used keyword-based search is often not sufficient, as it cannot capture the richness of visual content.
86. The Quiet Influence of AI: Instructions Without Words
AI-generated policies hide implicit commands. Silent mandates enforce compliance without explicit orders, reshaping institutional authority.
87. The Law Was Built for Humans—Now the Code Decides
An in-depth exploration of how legal frameworks fail to recognize executable authority in AI systems.
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