Do you want to teach AI on real projects?

In this #repository, there are 29 projects with Generative #AI,#MachineLearning, and #Deep +Learning.

With full #code for each one. This is pure gold: https://github.com/KalyanM45/AI-Project-Gallery

👉 https://t.iss.one/CodeProgrammer

💛 Top 10 Best Websites to Learn Machine Learning ⭐️
by [@codeprogrammer]

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🧠 Google’s ML Course
🔗 https://developers.google.com/machine-learning/crash-course

📈 Kaggle Courses
🔗 https://kaggle.com/learn

🧑‍🎓 Coursera – Andrew Ng’s ML Course
🔗 https://coursera.org/learn/machine-learning

⚡️ Fast.ai
🔗 https://fast.ai

🔧 Scikit-Learn Documentation
🔗 https://scikit-learn.org

📹 TensorFlow Tutorials
🔗 https://tensorflow.org/tutorials

🔥 PyTorch Tutorials
🔗 https://docs.pytorch.org/tutorials/

🏛️ MIT OpenCourseWare – Machine Learning
🔗 https://ocw.mit.edu/courses/6-867-machine-learning-fall-2006/

✍️ Towards Data Science (Blog)
🔗 https://towardsdatascience.com

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💡 Which one are you starting with? Drop a comment below! 👇
#MachineLearning #LearnML #DataScience #AI

https://t.iss.one/CodeProgrammer 🌟

GitHub has launched its learning platform: all #courses and certificates in one place.

#Git, #GitHub, #MCP, using #AI, #VSCode, and much more.

And most of the content is #free: → https://learn.github.com

👉 @codeprogrammer

9 key concepts of artificial intelligence, explained in 7 minutes

- Tokenization
- #TextDecoding
- #PromptEngineering
- Multi Step #AI Agents
- #RAGs
- #RLHF
- #VAE
- #DiffusionModels
- #LoRA

👉 @Python53

🔖 An excellent resource for learning about neural networks

We're sharing a cool resource for learning about neural networks, offering clear, step-by-step instruction with dynamic visualizations and easy-to-understand explanations.

In addition, you'll find many other useful materials on machine learning on the site.

Find and use it — https://mlu-explain.github.io/neural-networks/

tags: #AI #ML #PYTHON

➡ @CODEPROGRAMMER

🗂 One of the best resources for learning Data Science and Machine Learning

Kaggle offers interactive courses that will help you quickly understand the key topics of DS and ML.

The format is simple: short lessons, practical tasks, and a certificate upon completion — all for free.

Inside:

• basics of Python for data analysis;
• machine learning and working with models;
• pandas, SQL, visualization;
• advanced techniques and practical cases.

Each course takes just 3–5 hours and immediately provides practical knowledge for work.

➡ Link to the platform

tags: #ML #DEEPLEARNING #AI

➡ https://t.iss.one/CodeProgrammer

💾 6 AI courses from Anthropic for free

If you work with AI, agents, or APIs, this is the foundation that developers in top companies are currently going through.

▶️ Working with the Claude API

https://anthropic.skilljar.com/claude-with-the-anthropic-api 

▶️ Introduction to Model Context Protocol (MCP)

https://anthropic.skilljar.com/introduction-to-model-context-protocol 

▶️ Claude in Amazon Bedrock

https://anthropic.skilljar.com/claude-in-amazon-bedrock 

▶️ Claude in Google Cloud (Vertex AI)

https://anthropic.skilljar.com/claude-with-google-vertex 

▶️ Advanced MCP

https://anthropic.skilljar.com/model-context-protocol-advanced-topics 

▶️ Claude Code in Practice

https://anthropic.skilljar.com/claude-code-in-action 

tags: #courses #ai

➡ https://t.iss.one/CodeProgrammer

🤖 Best GitHub repositories to learn AI from scratch in 2026

If you want to understand AI not through "vacuum" courses, but through real open-source projects - here's a top list of repos that really lead you from the basics to practice:

1) Karpathy – Neural Networks: Zero to Hero 
The most understandable introduction to neural networks and backprop "in layman's terms"
https://github.com/karpathy/nn-zero-to-hero

2) Hugging Face Transformers 
The main library of modern NLP/LLM: models, tokenizers, fine-tuning 
https://github.com/huggingface/transformers

3) FastAI – Fastbook 
Practical DL training through projects and experiments 
https://github.com/fastai/fastbook

4) Made With ML 
ML as an engineering system: pipelines, production, deployment, monitoring 
https://github.com/GokuMohandas/Made-With-ML

5) Machine Learning System Design (Chip Huyen) 
How to build ML systems in real business: data, metrics, infrastructure 
https://github.com/chiphuyen/machine-learning-systems-design

6) Awesome Generative AI Guide 
A collection of materials on GenAI: from basics to practice 
https://github.com/aishwaryanr/awesome-generative-ai-guide

7) Dive into Deep Learning (D2L) 
One of the best books on DL + code + assignments 
https://github.com/d2l-ai/d2l-en

Save it for yourself - this is a base on which you can really grow into an ML/LLM engineer.

#Python #datascience #DataAnalysis #MachineLearning #AI #DeepLearning #LLMS

https://t.iss.one/CodeProgrammer