284 related articles

Want to become an AI Agent engineer? This article breaks down a 4-week roadmap: from core agent architecture and ReAct, to multi-agent collaboration and real projects.
CS Self-Study Guide: The 74K-Star Comp…
A 74K-star GitHub project by Peking University students curates MIT, Stanford, and CMU open courses into a complete CS self-study roadmap covering algorithms, OS, databases, and more.

A 3-month structured roadmap for developers transitioning into AI/LLM engineering: Python & API basics, LangChain/FastAPI stack, and RAG/Agent projects.
From Math to AI Research Engineer: A D…
A GitHub project called maths-cs-ai-compendium surpassed 6,000 Stars with a roadmap for becoming an AI/ML Research Engineer. Here's what makes it worth following.

A college student's MLOps 100-day challenge documents the full journey from Python engineering and Git to Docker, model deployment, and monitoring. A practical roadmap for data scientists transitioning to ML engineering.

A structured 4-week AI Agent learning roadmap: Week 1 covers LLMs & Prompt engineering, Week 2 ReAct paradigms, Week 3 RAG memory systems, Week 4 multi-agent architectures.

A structured zero-to-one roadmap for AI Agent development: Phase 1 covers Python & LLM basics, Phase 2 tackles five core Agent capabilities and LangChain/LangGraph, Phase 3 delivers hands-on RAG projects.

A complete 5-stage AI large model learning roadmap — from Python basics and prompt engineering to RAG pipelines, Agent development, and private model deployment.

A structured AI Agent learning roadmap covering 4 stages: foundations, core frameworks, scenario practice, and advanced product thinking. Master LangChain, tool calling, memory, and more.

New to AI Agents? This guide breaks down the full learning path — covering Agent principles, Prompt Engineering, RAG, multi-Agent systems, and hands-on projects to get you building fast.

Confused by scattered LLM resources and unclear learning paths? This guide maps a complete roadmap from basics to advanced, covering Karpathy, Stanford CS224N, DeepLearning.AI, Hugging Face, plus RAG, fine-tuning, and Agent deep dives.

AI talent gap is widening fast. Learn LLMs from zero in 3 months: Python & Transformer basics → Agents & LLMs → fine-tuning & private deployment. Land your AI job.

A complete AI Agent development learning roadmap covering three stages: Fundamentals (environment setup, tool use, memory), Advanced (multi-agent systems, RAG, ReAct), and Practical Projects (enterprise chatbots, automation tools).
The Complete AI Researcher Learning Ro…
A structured AI/ML learning roadmap covering Python, math, machine learning, deep learning, and MLOps — with timelines, milestones, and free resource recommendations.

Calling an API isn't enough. This article breaks down the full AI application developer skill structure — Python, deep learning, fine-tuning, Agents, and enterprise projects — with a clear learning roadmap.

90% of AI beginners struggle with large language models due to misdirection, poor Prompt logic, and lack of real-world deployment skills. This guide covers the complete learning path from zero to practice.

A structured AI Agent learning roadmap covering fundamentals (Agent principles, Prompt engineering), advanced topics (RAG, multi-agent collaboration), and three hands-on projects — ideal for beginners.

Can you learn MLOps from scratch? This guide breaks down core skill requirements and offers a practical 4-phase, 24-month roadmap covering Python, ML, DevOps, and MLflow.

Struggling with math for ML? This guide covers linear algebra, calculus, probability, and optimization with top resources like 3Blue1Brown and Mathematics for Machine Learning.

A self-learner completed a full progression from math foundations and core ML to deep learning in 6 months—hand-writing a Transformer and implementing gradient boosting from scratch. This article breaks down the highlights and blind spots of this real roadmap.