Everyone talks about "learning AI."

Almost nobody tells you where to actually start.

Over the past few months, I've been learning AI, ML, Cloud, and Agentic AI. Instead of bookmarking hundreds of random links, I narrowed it down to the resources that genuinely helped me understand and build things.

If you're starting your AI journey, this list should save you a lot of time.

📌 Save this post—you'll probably come back to it later.

🤖 Generative AI

• Google AI Studio — https://aistudio.google.com/
• OpenAI Cookbook — https://cookbook.openai.com/
• Hugging Face Learn — https://huggingface.co/learn
• DeepLearning AI Short Courses — https://www.deeplearning.ai/short-courses/
• Anthropic Prompt Engineering Guide — https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview

☁️ Cloud

• Google Cloud Skills Boost — https://www.cloudskillsboost.google/
• Microsoft Learn — https://learn.microsoft.com/
• AWS Skill Builder — https://skillbuilder.aws/
• Oracle MyLearn — https://mylearn.oracle.com/

📊 Machine Learning

• Kaggle Learn — https://www.kaggle.com/learn
• Stanford CS229 — https://cs229.stanford.edu/
• Andrew Ng – Machine Learning Specialization — https://www.coursera.org/specializations/machine-learning-introduction

🧠 LLMs & AI Agents

• LangChain Documentation — https://python.langchain.com/
• LlamaIndex Documentation — https://docs.llamaindex.ai/
• Model Context Protocol (MCP) — https://modelcontextprotocol.io/
• Google Agent Development Kit (ADK) — https://google.github.io/adk-docs/
• OpenAI Agents SDK — https://openai.github.io/openai-agents-python/
• CrewAI — https://docs.crewai.com/
• Microsoft AutoGen — https://microsoft.github.io/autogen/

🐍 Python

• CS50's Introduction to Python — https://cs50.harvard.edu/python/
• Python for Everybody — https://www.py4e.com/

📈 Data Science

• NumPy Documentation — https://numpy.org/doc/
• Pandas Documentation — https://pandas.pydata.org/docs/
• scikit-learn Documentation — https://scikit-learn.org/stable/

🛠️ AI Development Tools

• Google Colab — https://colab.research.google.com/
• Kaggle Notebooks — https://www.kaggle.com/code
• Jupyter Notebook — https://jupyter.org/
• Weights & Biases — https://wandb.ai/
• Ollama — https://ollama.com/

💻 Coding Practice

• LeetCode — https://leetcode.com/
• NeetCode — https://neetcode.io/
• Exercism — https://exercism.org/
• Frontend Mentor — https://www.frontendmentor.io/

🏆 Competitions

• Kaggle Competitions — https://www.kaggle.com/competitions
• AIcrowd — https://www.aicrowd.com/

📚 Research Papers

• Papers with Code — https://paperswithcode.com/
• arXiv — https://arxiv.org/
• Semantic Scholar — https://www.semanticscholar.org/
• Distill — https://distill.pub/

⭐ Bonus (Open Source AI)

• Awesome Generative AI — https://github.com/steven2358/awesome-generative-ai
• Awesome LLM Apps — https://github.com/Shubhamsaboo/awesome-llm-apps
• Microsoft AI-For-Beginners — https://github.com/microsoft/AI-For-Beginners
• Microsoft Generative AI for Beginners — https://github.com/microsoft/generative-ai-for-beginners
• Microsoft Zero to Agentic AI (Free) — https://learn.microsoft.com/collections/6q08f7tjr3yg4m/?wt.mc_id=studentamb_523020

🔥 My honest advice

Don't try to learn everything at once. Pick one roadmap, build real projects with it, and stay consistent. Consistency beats hoarding tabs.

What free resource actually moved the needle for you? Drop it below — I'll add the best ones to the list with credit.