I want to get into AI Engineering

Want to get into AI Engineering? Save this and come back to it. Watching videos alone won’t make you an AI engineer. Building things will. These are some of the best resources to help you build real projects and understand the concepts that actually matter. Literally, 90% of the people who watch all of these, build alongside them, and truly understand the concepts will know more than 90% of people just consuming AI content online. Don’t just watch. Build every project alongside the videos. 1. Python – Python for AI – Full Beginner Course by Dave Ebbelaar 2. Neural Networks – The spelled-out intro to neural networks and backpropagation: building micrograd by Andrej Karpathy 3. FastAPI – Python FastAPI Tutorial: Getting Started – Web App + REST API by Corey Schafer 4. RAG with LangChain – Complete RAG Crash Course With LangChain In 2 Hours by Krish Naik 5. LangGraph – LangGraph Complete Course for Beginners – Complex AI Agents with Python by freeCodeCamp.org 6. AI Agents – AI Agents in 38 Minutes – Complete Course from Beginner to Pro by Marina Wyss – AI & Machine Learning 7. MCP – Model Context Protocol Clearly Explained | MCP Beyond the Hype by codebasics 8. LLMs – Stanford CS229: Building Large Language Models (LLMs) by Stanford Online 9. Fine-tuning – LLM Fine-Tuning Course – From Supervised FT to RLHF, LoRA, and Multimodal by freeCodeCamp.org 10. LLMOps – Agentic RAG & LLMOps – How Observability Helps (LangGraph & Opik) by Jam With AI Disclaimer: Some of these videos may be older, but the foundations they teach are still essential. AI evolves quickly, so while learning from these resources, always build using the latest documentation and best practices. . . . [AI Engineering, AI Engineer Roadmap, Artificial Intelligence, Machine Learning, Python, Python for AI, Neural Networks, Deep Learning, Backpropagation, LLM, Large Language Models, Generative AI, FastAPI, REST API, RAG, Retrieval-Augmented Generation, LangChain, LangGraph, AI Agents, Agentic AI, MCP, Model Context Protocol, Fine-Tuning, LoRA, RLHF, LLMOps, AI Observability, Vector Databases] #coding #study