A series of Jupyter notebooks teaches machine learning and deep learning in Python with Scikit-Learn, Keras, and TensorFlow.
The Best Machine Learning Visualized Alternatives in 2026
Looking for something like Machine Learning Visualized? Here are 24 alternatives, ranked by how closely they match it on purpose, features and platforms. Of these, 11 are open source.
Jupyter notebooks implement and derive machine learning algorithms from first principles using NumPy.
Top picks at a glance
Quick comparison
| # | Alternative | Pricing | Open source | Platforms |
|---|---|---|---|---|
| 1 | Handson ML3 | — | Yes | — |
| 2 | Hands-on Train and Deploy ML | — | Yes | — |
| 3 | Learn-Python | — | Yes | — |
| 4 | Data Science Ipython Notebooks | — | Not stated | — |
| 5 | rasbt/LLMs-from-scratch | — | Not stated | — |
| 6 | ML Engineering | — | Not stated | — |
| 7 | Machine Learning Roadmap | — | Not stated | Web |
| 8 | Codecademy's Data Science | — | Not stated | Web |
| 9 | LLM Course | — | Yes | — |
| 10 | ML YouTube Courses | — | Yes | — |
All Machine Learning Visualized alternatives
This tutorial walks through training and deploying a machine learning REST API that predicts cryptocurrency prices in ten steps.
A Python learning playground and cheat sheet organizes example scripts by topic, with explanations for the code.
Data Science Ipython Notebooks
github.com/donnemartin/data-science-ipython-no…Data Science IPython Notebooks collects notebooks about data science, including TensorFlow deep learning.
rasbt/LLMs-from-scratch
github.com/rasbt/LLMs-from-scratchThis repository walks through implementing a ChatGPT like language model in PyTorch, step by step.
ML Engineering
github.com/stas00/ml-engineeringThis open book collects methods, tools, and instructions for training, fine-tuning, and running machine learning models.
Machine Learning Roadmap
rentry.org/machine-learning-roadmapThis machine learning roadmap lays out a learning path that starts with mathematics and calculus fundamentals.
Codecademy's Data Science
codecademy.com/learn/paths/data-scienceCodecademy’s Machine Learning Specialist path covers Python, SQL, pandas, scikit-learn, Matplotlib, and TensorFlow.
This LLM course combines learning roadmaps with Google Colab notebooks for studying large language models.
A curated repository helps readers discover machine learning and AI courses published on YouTube.
Algorithms Cheatsheet Resources collects reference material for programming and algorithms.
Virgilio
github.com/virgili0/VirgilioAn open-source initiative, Virgilio guides learners through data science resources and skill development.
Scipy Lectures
lectures.scientific-python.orgScientific Python Lectures is a set of learning materials about Python for scientific computing.
IAILab
iailab.kaist.ac.kr/teaching/deep-learningThe iAI KAIST page shares deep learning materials that visitors are permitted to use, modify, and distribute.
Virgilio
virgili0.github.io/VirgilioVirgilio organizes data science e-learning resources in a structured project overview.
Sourcecodester
sourcecodester.comSourcecodester publishes free source code projects and tutorials in programming languages including Python, PHP, and Java.
Tpoint Tech
tpointtech.comTpoint Tech publishes free online tutorials and interview questions across programming languages and technology subjects.
ItSourceCode
itsourcecode.comItSourceCode offers free source code, capstone projects, and programming tutorials in several languages.
Coding Problems provides solutions to coding and algorithm problems and multiple programming exercises.
Awesome YouTubers collects channels that teach a range of subjects through video.
AI for Beginners is a Microsoft course organized as 24 lessons across 12 weeks.
This repository documents a 90-day cybersecurity study plan with topics for a staged learning schedule.
Machine Learning for Software Engineers
github.com/ZuzooVn/machine-learning-for-softwa…A daily study plan for people working toward becoming machine learning engineers.
A curated collection gathers learning resources for generative AI, machine learning, LLMs, RAG, and related topics.
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