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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
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The 25 Best Free Resources for Learning AI in 2026
Learning artificial intelligence in 2026 is both easier and harder than it was a couple of years ago. Easier, because the best courses from Stanford and MIT, books by leading researchers, and hands-on lessons from Hugging Face are all openly available. Harder, because there is now far too much material, and the models and tools change every week. We have collected 25 proven free resources and arranged them by stage: from mathematics to LLMs, agents, and reading the latest papers.
Read: https://enghusseinsheikho.substack.com/p/the-25-best-free-resources-for-learning?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
🔖 637 interactive modules on ML and AI
On VizLearn, you can visually explore:
🫡 Gradient descent;
🫡 SVM and PCA;
🫡 Bayes' theorem;
🫡 BPE tokenization;
🫡 Query, Key, and Value;
🫡 KV cache;
🫡 Quantization;
🫡 Neural networks and LLMs.
Change the input data and immediately see the results of the calculations.
⛓ Link to the tutorial
https://vizlearn.in/
💾 Free Deep Learning Course from NASA
We found a practical course on applying modern AI methods in astronomy and astrophysics.
It covers neural network architectures, generative models, training on scientific data, simulation-based inference, and research AI agents.
⛓ Link to the course
https://deeplearning4astro.com/
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⭐️ If you want to get dangerously good at generative AI,
then save these 10 GitHub repos:
1️⃣ Generative AI for Beginners
→ https://github.com/microsoft/generative-ai-for-beginners
2️⃣ LLM Course
→ https://github.com/mlabonne/llm-course
3️⃣ Build a Large Language Model From Scratch
→ https://github.com/rasbt/LLMs-from-scratch
4️⃣ Prompt Engineering Guide
→ https://github.com/dair-ai/Prompt-Engineering-Guide
5️⃣ Awesome LLM Apps
→ https://github.com/Shubhamsaboo/awesome-llm-apps
6️⃣ OpenAI Cookbook
→ https://github.com/openai/openai-cookbook
7️⃣ Hands-On Large Language Models
→ https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
8️⃣ GenAI Agents
→ https://github.com/NirDiamant/GenAI_Agents
9️⃣ Awesome Generative AI Guide
→ https://github.com/aishwaryanr/awesome-generative-ai-guide
🔟 Anthropic Courses
→ https://github.com/anthropics/courses
💬 Anything else you'd add to this list?
===
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Agentic AI is the future.
If you want to become god-level with AI agents, save these 10 GitHub repos:
1 Awesome LLM Apps
🔗 https://github.com/Shubhamsaboo/awesome-llm-apps
2 Generative AI for Beginners
🔗 https://github.com/microsoft/generative-ai-for-beginners
3 LLM Course
🔗 https://github.com/mlabonne/llm-course
4 Prompt Engineering Guide
🔗 https://github.com/dair-ai/Prompt-Engineering-Guide
5 AI Agents for Beginners
🔗 https://github.com/microsoft/ai-agents-for-beginners
6 AI Agents Projects
🔗 https://github.com/ashishpatel26/500-AI-Agents-Projects
7 Hugging Face Agents Course
🔗 https://github.com/huggingface/agents-course
8 Awesome AI Agents
🔗 https://github.com/e2b-dev/awesome-ai-agents
9 GenAI Agents
🔗 https://github.com/NirDiamant/GenAI_Agents
10 Agents Towards Production
🔗 https://github.com/NirDiamant/agents-towards-production
Anything else you'd add?
📚 7 Best Websites to Learn Computer Science Subjects for Free
More: https://enghusseinsheikho.substack.com/p/7-best-websites-to-learn-computer?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
🔖 A Comprehensive Resource for Machine Learning Practice
The platform offers:
🫡 1200+ machine learning tasks;
🫡 Mathematics, from linear algebra to PCA;
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Almost everything can be accessed directly in your browser, and basic tasks are available for free.
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"Graph Theory with Applications" is a free textbook that provides a comprehensive introduction to graph theory and its practical applications.
The book begins with basic concepts: graphs, subgraphs, paths, cycles, trees, and connectivity. It then moves on to more advanced topics: Eulerian and Hamiltonian graphs, matching, graph coloring, planar and directed graphs, network flows, and extremal graph theory.
What is particularly useful is that the authors combine a visual, geometric, and topological understanding of graphs with a rigorous presentation of the mathematical foundations.
This makes the book a valuable resource for studying the theory that underlies many modern applications of graphs in computer science, computer networks, optimization, data analysis, and related fields.
Book: https://book.huihoo.com/pdf/graph-theory-with-applications.pdf
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One of the key moments when I truly understood how transformers work: 🧠✨
"Stop thinking of a transformer as a conveyor belt, where each layer transforms the output of the previous one."
"Start thinking of it as a residual flow." 🌊
Each block in a transformer has a residual connection that adds the input of the block to its output:
x' = f(x) + x
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I found a great resource for interactive learning about machine learning and AI – VizLearn.
You can experiment with gradient descent, SVM, PCA, the Bayesian method, BPE tokenization, Q/K/V, KV-cache, quantization, and much more. You can change the input data and see how the calculations themselves change.
It's free and doesn't require registration.
https://vizlearn.in
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"How to Train a Neural Network" is a concise summary of the MIT course lectures on deep learning from 2024. It focuses on one of the fundamental questions in neural networks: how a model learns its weights.
The summary examines the training process from a mathematical perspective. It covers topics such as forward propagation, loss functions, gradients, backpropagation, and gradient-based optimization methods.
I believe this is an interesting resource for those who want to go beyond a general, intuitive understanding of neural networks and begin to delve into the mathematics that underlies their training.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/mit6_7960_f24_lec2.pdf
/channel/CodeProgrammer 🤩
🔖 Interactive CNN Layer-by-Layer Explanation
With the CNN Explainer, you can learn about:
🫡 Input RGB channels;
🫡 Convolution;
🫡 ReLU;
🫡 Max Pooling;
🫡 Activation maps;
🫡 Flatten;
🫡 Softmax;
🫡 Final prediction.
You can upload your own image and see how the network processes it step by step.
⛓ https://poloclub.github.io/cnn-explainer/
Agentic AI Roadmap 2026
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If you’re prepping for cloud, dev, or AI roles this year, these free kits cover the gaps most people skip — cert basics, hands-on labs, and mock exams. No signup traps, no hidden fees.
📘 Cert E-Book — covers Cisco & AWS fundamentals, good for first-time cert takers
→ https://bit.ly/4yrk1Ti
🪜 Online Free Course — structured path, works as a refresher before exams
→ https://bit.ly/4hUprRP
☁️ Free AI Materials — LLM basics to fine-tuning practice notebooks
→ https://bit.ly/4yt1iXy
📊 Cloud Study Guide — cheat-sheet style, handy for interview prep
→ https://bit.ly/4el4Eod
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→ https://bit.ly/4sbWlkl
📌 Save this or forward it to your dev/study group — good resources are worth sharing. 💪
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😰 15-Hour "Python for Data Science" Course
⚡️ Learn Python for Data Science
https://enghusseinsheikho.substack.com/p/15-hour-python-for-data-science-course?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
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If you want to get GOAT-level at software engineering,
save & star these 10 GitHub repos:
1 System design academy
↳ https://github.com/systemdesign42/system-design-academy
2 Public APIs
↳ https://github.com/public-apis/public-apis
3 Tech interview handbook
↳ https://github.com/yangshun/tech-interview-handbook
4 Coding interview university
↳ https://github.com/jwasham/coding-interview-university
5 Engineering leadership
↳ https://github.com/gregorojstersek/resources-to-become-a-great-engineering-leader
6 Freecodecamp
↳ https://github.com/freeCodeCamp/freeCodeCamp
7 Developer roadmaps
↳ https://github.com/kamranahmedse/developer-roadmap
8 Path to senior engineer handbook
↳ https://github.com/jordan-cutler/path-to-senior-engineer-handbook
9 Free programming books
↳ https://github.com/EbookFoundation/free-programming-books
10 Build your own x
↳ https://github.com/codecrafters-io/build-your-own-x
Anything else you'd add?
♻ Repost to help others become better engineers.
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Learn Deep Learning Cheat Sheet
@followers #DeepLearning #neuralnetworks #ArtificialIntelligence #machinelearning #ArtificialNeuralNetworks
husseinsheikho?utm_source=share&utm_medium=android&r=3hwnr8" rel="nofollow">https://substack.com/@husseinsheikho?utm_source=share&utm_medium=android&r=3hwnr8
Follow me to more posts and useful content
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📚 Download 10 Python books for FREE:
1. Think Python — O’Reilly
https://t.co/X7y3pX68IW
2. The Big Book of Small Python Projects
https://inventwithpython.com/bigbookpython/
3. Data Structures
https://opendatastructures.org/ods-python.pdf
4. Data Science Handbook
https://t.co/9aOLdiAbim
5. Data Analysis
https://wesmckinney.com/book/
6. Data Science
https://t.co/TusyBWGfrx
7. Machine Learning
https://t.co/7veOwESe8q
8. Statistics
https://t.co/waWDQylQpY
9. Think Bayes
https://allendowney.github.io/ThinkBayes2/
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https://t.co/OHhVgF1L65
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🔖 Official PyTorch Tutorials
This collection includes:
🫡 PyTorch fundamentals and torch.nn;
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torch.compile
Understanding algorithms changes the way you write code. 🧠 This playlist teaches you exactly that: understanding.
Pavel Mavrin. 61 lectures. Free. 🎓
Here's the difference:
Before understanding algorithms:
- You use hash tables for everything.
- You write the code first, then optimize it.
- You estimate the time complexity randomly.
After:
- You choose the appropriate data structure for the task.
- You think about complexity before writing the code.
- You learn an approach that is suitable for solving the problem.
What you will learn:
→ Time complexity and sorting.
→ Dynamic programming.
→ Advanced trees: segment tree, Fenwick tree, splay tree, link-cut tree.
→ Graph algorithms: traversals, shortest paths, flows.
→ String algorithms: substring search, suffix structures.
→ Fast Fourier Transform, linear programming, cryptography.
https://www.youtube.com/playlist?list=PLrS21S1jm43igE57Ye_edwds_iL7ZOAG4
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A collection of explanations for key concepts and scientific papers in machine learning:
https://github.com/dair-ai/ML-Papers-Explained