AI Career Guidelines
🚀 Free AI & Career Resource Hub
Welcome to the code2career_ai library. We believe the future of engineering belongs to those who can build, not just prompt. Below is a curated collection of the best free resources to take you from a standard developer to an AI Architect.
📚 1. The "Agentic" AI Stack (Build the Future)
Master the tools required to build autonomous agents and complex workflows.
LangGraph & LangChain
[Must Watch] AI Agents in LangGraph (DeepLearning.AI)
Why: Taught by Harrison Chase (creator of LangChain). It covers the exact cyclic graph architecture we discuss in our articles.
Link: Start Learning
LangChain & Vector Databases in Production
Why: A practical guide to RAG (Retrieval-Augmented Generation).
Link: ActiveLoop Course
Vector Databases (The Memory of AI)
Vector Databases for RAG (Coursera/DeepLearning.AI)
Why: Understanding embeddings is non-negotiable. This course explains how to store and retrieve context for LLMs.
Link: View Course
Pinecone "Learning Center"
Why: Excellent, documentation-style guides on how vector search works under the hood.
Link: Pinecone Learn
☁️ 2. Cloud & Data Engineering for AI
AI doesn't exist in a vacuum—it lives on data pipelines.
Snowflake & Data Engineering
Snowflake Hands-On Essentials
Why: Earn badges while learning the core architecture of the Data Cloud.
Link: Snowflake University
Snowpark for Machine Learning (Tutorial)
Why: Learn to run Python and ML models directly inside Snowflake without moving data.
Link: Snowpark ML Quickstart
Azure OpenAI & Cloud Fundamentals
Azure AI Fundamentals (AI-900)
Why: The foundational certification for the Microsoft stack.
Link: Microsoft Learn Path
Generative AI for Beginners (Microsoft)
Why: A 12-lesson curriculum on GitHub building real apps.
Link: GitHub Repository
🧠 3. Foundational Generative AI
Strong foundations prevent "hallucinations" in your career.
Google Cloud Skills Boost: Generative AI Path
What: From "What is GenAI" to "Encoder-Decoder Architecture" and "Transformer Models."
ChatGPT Prompt Engineering for Developers
What: The classic course by Andrew Ng and OpenAI. Essential for understanding how to talk to models programmatically.
Link: DeepLearning.AI
🗺️ 4. Career Roadmaps & Guides
Visual paths to help you navigate the chaos.
AI Engineer Roadmap (roadmap.sh)
Description: A visual, step-by-step tree of skills required for 2024/2025, from Mathematics to MLOps.
Link: View Roadmap (Note: roadmap.sh is the gold standard for visual tech guides)
📚 5. DeepLearning.AI & Microsoft Short Courses
Free, high-quality courses to master specific AI skills.
Microsoft Generative AI for Beginners
Description: 21 lessons covering every core concept you need to start building Generative AI applications.
Link: Start Learning
Function Calling and Data Extraction with LLMs
Description: Master function-calling to connect LLMs with real tools and pull structured data with precision.
Link: Take the Course
Building Your Own Database Agent
Description: Learn to interact with SQL databases using natural language. Beginner-friendly.
Link: Start Building
LangChain for LLM App Development
Description: Build real LLM applications step by step using LangChain. Great for beginners with some Python basics.
Link: Learn LangChain
Building and Evaluating Advanced RAG Apps
Description: Learn cutting-edge Retrieval-Augmented Generation techniques and evaluation. Perfect if you have basic Python knowledge.
Link: Master RAG
Building Agentic RAG with LlamaIndex
Description: Quickly build agents that reason over your documents. Ideal for anyone who knows basic Python.
Link: Build with LlamaIndex
🔔 Keep Updated
Content in this space moves fast. I regularly update this page with new findings. If you find a broken link or a great free resource, let me know on LinkedIn!