Machine Learning with Python

The most complete list of video courses on Computer Science on the internet.

cs-video-courses — 78K+ stars.

MIT.
Stanford University.
University of California, Berkeley.
Harvard University.
Carnegie Mellon University.
Indian Institutes of Technology.
Princeton University.
California Institute of Technology.

Everything is free. All lectures are in video format. Everything is collected in one repository.

Topics:

→ Data structures and algorithms
→ Operating systems
→ Distributed systems
→ Database systems
→ Computer networks
→ Machine learning
→ Deep learning
→ Natural language processing (NLP)
→ Computer vision
→ Computer graphics
→ Security
→ Quantum computing
→ Robotics
→ Blockchain

From beginner level (CS50) to advanced (6.824 Distributed Systems).

The curriculum is free. 🤙
https://github.com/Developer-Y/cs-video-courses

https://t.me/CodeProgrammer ⚡️
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Please note that the permanent subscription to our Premium channel will be permanently closed in five days.

The cost of a permanent subscription to our premium channel is $35.

The Premium channel contains thousands of books and courses available for free as direct downloadable Telegram files.

Contact me @HusseinSheikho

When have you ever needed to add a mathematical description for your function in Python, but found that it takes too much time?

Non-programmers can't easily read Python's logic. However, manually converting it to LaTeX is slow and quickly becomes outdated as the code changes.

latexify_py solves this problem with a single decorator, generating LaTeX directly from your function, so that the mathematics remains readable and always synchronized with the code.

Main features:
• Three decorators for different outputs: expressions, full equations, or pseudocode
• Displays the rendered LaTeX directly in Jupyter cells
• Functions continue to work normally when called

In addition, latexify_py is open source. Install it using pip install latexify-py

An article about 3 tools that convert Python code to LaTeX: https://bit.ly/3Pw89yP
Run this code: https://bit.ly/4bW2ycE

https://t.me/CodeProgrammer

💻 Collection of cheat sheets on SQL

I've gathered for you short and understandable cheat sheets on the main topics:
▶️ Basics of the SQL language;
▶️ JOINs with clear examples;
▶️ Window functions;
▶️ SQL for data analysis.

An excellent set to refresh your knowledge before a job interview or quickly recall the syntax.

tags: #sql #useful

https://t.me/DataAnalyticsX

Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🗂 20 free MIT courses — the entire Computer Science base in one place

#MIT has made courses in key CS areas publicly available. #Python, #algorithms, #ML, neural networks, #OS, #databases, #mathematics — all can be completed for free directly on #YouTube.

▶️ Introduction to Python Programming
▶️ Data Structures and Algorithms
▶️ Mathematics for Computer Science
▶️ Machine Learning
▶️ Deep Learning
▶️ Artificial Intelligence
▶️ Machine Learning in Healthcare
▶️ Database Management Systems
▶️ Operating Systems
▶️ One-Variable Calculus
▶️ Many-Variable Calculus
▶️ Introduction to Probability Theory
▶️ Statistics
▶️ Probability Theory and Statistics
▶️ Linear Algebra
▶️ Matrix Calculus for Machine Learning
▶️ Java Programming
▶️ Design and Analysis of Algorithms
▶️ Advanced Data Structures
▶️ Introduction to Computational Thinking

tags: #courses

➡https://t.me/python53

🐼 Cheat Sheet on Data Wrangling — for everyone who works with Pandas

Everything you need is collected in one file: creating and merging DataFrames, filtering, grouping, handling missing values, and visualization.

It's convenient when you need to quickly refresh your syntax and don't want to dig into the documentation.

The cheat sheet in good quality
https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf

tags: #useful

For more please ❤️

➡ https://t.me/CodeProgrammer

RAG won't work in 2026 if you're still using old approaches.

Yes, many companies are still failing with RAG — not because they're doing it wrong, but because they're stuck on outdated techniques.

Here's what usually happens: most companies start with a chatbot / chat app when talking about AI implementation. And here RAG becomes key — to connect their data via a database and enable the chat app to retrieve relevant documents.

But today, RAG is no longer limited to just chats. The applications of RAG are practically limitless, and that's a good thing.

RAG still remains the foundation for everything you build on LLMs and AI agents. The only thing that's changed is the RAG techniques themselves. The old approach no longer works — more advanced techniques are needed, what's now called advanced RAG.

The essence of RAG is to enrich the system with your data via a database so it can find relevant documents or their parts. The results are simple and often "okay", especially if the documents are well-structured and there aren't many of them.

But when the documents are unstructured and it's important to get not just accurate documents but also the right context, advanced techniques come into play:

- query decomposition
- metadata enrichment
- hybrid indexing
- reranking
- context fusion

These approaches allow the RAG system to find and generate more accurate and contextually relevant answers.

Therefore, advanced RAG is important. RAG isn't dead and can't die. Just use smarter techniques.

💾 TensorTonic — a platform with 200+ algorithms and tasks for ML developers

What's inside:
▶️ Analysis of research and step-by-step reproduction of model architectures;
▶️ Explanation of topics and concepts with interactive visualizations;
▶️ A progress and achievement system — what would we do without gamification.

A great option to hone your ML skills in the evening ⚡

https://www.tensortonic.com/

Classical filters & convolution: The heart of computer vision

Before Deep Learning exploded onto the scene, traditional computer vision centered on filters. Filters were small, hand-engineered matrices that you convolved with an image to detect specific features like edges, corners, or textures. In this article, we will dive into the details of classical filters and convolution operation - how they work, why they matter, and how to implement them.

More: https://www.vizuaranewsletter.com/p/classical-filters-and-convolution

📱 Python enthusiasts, this is for you — 15 BEST REPOSITORIES on GitHub for learning Python

▶️ Awesome Python — https://github.com/vinta/awesome-python
— the largest and most authoritative collection of frameworks, libraries, and resources for Python — a must-save

▶️ TheAlgorithms/Python — https://github.com/TheAlgorithms/Python
— a huge collection of algorithms and data structures written in Python

▶️ Project-Based-Learning — https://github.com/practical-tutorials/project-based-learning
— learning Python (and not only) through real projects

▶️ Real Python Guide — https://github.com/realpython/python-guide
— a high-quality guide to the Python ecosystem, tools, and best practices

▶️ Materials from Real Python — https://github.com/realpython/materials
— a collection of code and projects for Real Python articles and courses

▶️ Learn Python — https://github.com/trekhleb/learn-python
— a reference with explanations, examples, and exercises

▶️ Learn Python 3 — https://github.com/jerry-git/learn-python3
— a convenient guide to modern Python 3 with tasks

▶️ Python Reference — https://github.com/rasbt/python_reference
— cheat sheets, scripts, and useful tips from one of the most respected Python authors

▶️ 30-Days-Of-Python — https://github.com/Asabeneh/30-Days-Of-Python
— a 30-day challenge: from syntax to more complex topics

▶️ Python Programming Exercises — https://github.com/zhiwehu/Python-programming-exercises
— 100+ Python tasks with answers

▶️ Coding Problems — https://github.com/MTrajK/coding-problems
— tasks on algorithms and data structures, including for preparation for interviews

▶️ Projects — https://github.com/karan/Projects
— a list of ideas for pet projects (not just Python). Great for practice

▶️ 100-Days-Of-ML-Code — https://github.com/Avik-Jain/100-Days-Of-ML-Code
— machine learning in Python in the format of a challenge

▶️ 30-Seconds-of-Python — https://github.com/30-seconds/30-seconds-of-python
— useful snippets and tricks for everyday tasks

▶️ Geekcomputers/Python — https://github.com/geekcomputers/Python
— various scripts: from working with the network to automation tasks

React ♥️ for more posts like this 💛

🗂 Harvard has released a textbook on ML systems — from autograd to production

Not just another "what is a neural network" course — this is about how to build combat-ready ML systems around models.

What's inside:
▶️ Building autograd, optimizers, attention, and mini-PyTorch from scratch;
▶️ Batches, computational accuracy, architectures, and training;
▶️ Performance optimization, hardware acceleration, and benchmarking.

You can read the book and the code for free right now.

✈️ Link to GitHub
https://github.com/harvard-edge/cs249r_book

📱 A huge cheat sheet for Python, Django, Plotly, Matplotlib, Pygame

Many topics are covered inside:
🔸 All basic constructs: variables, conditions, loops, lists, dictionaries, functions, and classes — with clear examples;

🔸 Working with files, exceptions, and data input — understandable even for beginners;

🔸 #Django, #Pygame, #Matplotlib, and #Plotly — brief instructions on how to get started with each of the frameworks;

🔸 Tips on #Git, project structure, and unit testing.

https://t.me/CodeProgrammer ❤️