250 Coursera FREE Courses [Data Science, Machine Learning, Python] 2024 🐍

🔗 Link: https://www.mltut.com/coursera-free-courses/

📂 Tags: #DataScience #Python #ML #AI #courses

http://xn--r1a.website/codeprogrammer ⭐️

The opportunity is not too late, register before it is too late  😮

Here: GitHub repository to learn AI Engineering.

It contains some of the best free courses, articles, tutorials, and videos on the following topics:

Mathematical foundation
Basics of AI and #ML
Deep Learning and specializations
Generative #AI
Large language models (#LLM)
Guides on #promptengineering
#RAG, #agents, and #MCP

See here: https://github.com/ashishps1/learn-ai-engineering

👉 @CODEPROGRAMMER

🗂 Building our own mini-Skynet — a collection of 10 powerful AI repositories from big tech companies

1. Generative AI for Beginners and AI Agents for Beginners
Microsoft provides a detailed explanation of generative AI and agent architecture: from theory to practice.

2. LLMs from Scratch
Step-by-step assembly of your own GPT to understand how LLMs are structured "under the hood".

3. OpenAI Cookbook
An official set of examples for working with APIs, RAG systems, and integrating AI into production from OpenAI.

4. Segment Anything and Stable Diffusion
Classic tools for computer vision and image generation from Meta and the CompVis research team.

5. Python 100 Days and Python Data Science Handbook
A powerful resource for Python and data analysis.

6. LLM App Templates and ML for Beginners
Ready-made app templates with LLMs and a structured course on classic machine learning.

If you want to delve deeply into AI or start building your own projects — this is an excellent starting kit.

tags: #github #LLM #AI #ML

➡️ https://xn--r1a.website/CodeProgrammer

🛫 ML Roadmap 2026 — a comprehensive guide to entering ML, LLM, and MLOps

A rather insightful ML roadmap has gone viral on GitHub: within it, the author has compiled a path from a foundation in mathematics, NumPy, and Pandas to LLM, agentic RAG, fine-tuning, MLOps, and interview preparation. The repository indeed includes sections on Karpathy, MCP, RLHF, LoRA/PEFT, and system design for AI interviews.

Conveniently, this isn't just a list of random links, but rather a structured route through the topics:
▶️ Foundations and tools;
▶️ Classic ML;
▶️ LLM and agents;
▶️ Engineering and MLOps;
▶️ Interview preparation.

➡️ GitHub link:
https://github.com/loganthorneloe/ml-roadmap

tags: #ml #llm

➡ https://xn--r1a.website/CodeProgrammer

🔖 10 Stanford courses on AI and ML — with official pages and all materials

▶️ CS221: Artificial Intelligence
▶️ CS229: Machine Learning
▶️ CS229M: Theory of Machine Learning
▶️ CS230: Deep Learning
▶️ CS234: Reinforcement Learning
▶️ CS224N: Natural Language Processing
▶️ CS231N: Deep Learning for Computer Vision
▶️ CME295: Large Language Models
▶️ CS236: Deep Generative Models
▶️ CS336: Modeling Language from Scratch

They cover the entire spectrum: classic ML, LLM, and generative models — with theory and practice.

tags: #python #ML #LLM #AI

➡ https://xn--r1a.website/MachineLearning9

LLMs are the new operating system for work. 🚀💻

But most people still don’t know the difference between RAG, Embeddings, and Hallucinations. 🤔🧠

Here’s the vocabulary cheat sheet everyone in AI should know 📚✨

These foundational LLM concepts every professional, creator, founder, and tech enthusiast should know 👩‍💼👨‍💻🎨🚀

#LLM #DataScience #AI #ML

https://xn--r1a.website/DataAnalyticsX 📎