The AI Learning Adventure: Your Roadmap to Knowledge
Two AI-related posts in a row? Well, hype is hype! One way to cut through it is to learn about the subject matter and use that knowledge to distinguish between what’s genuine and what’s merely a sales pitch. (I promise this is the last AI-related post this year)
Over the past year, I’ve invested a significant amount of time studying the field of AI, using a variety of resources as my guide. Today, I’m excited to share this list with you, so you can navigate the vast ocean of online content and find the best resources available. I’ll categorize the material into several areas, highlight the most important aspects within each category, and recommend resources in multiple formats - like articles, videos and books - tailored to different learning styles.
I hope you find this list helpful in figuring out what to learn. Happy studying!
Artificial Intelligence (AI)
What you should learn:
- Definition of Artificial Intelligence (AI)
- Types of problems AI can effectively address
- Challenges and considerations associated with AI: security, ethics, and accuracy
Learning resources:
- 🎥 Google’s AI Course for Beginners (in 10 minutes)!
- 📜 What Is Artificial Intelligence (AI)? | Google Cloud
Machine Learning
What you should learn:
- Definition and purpose of a machine learning model
- Understanding features and labels in datasets
- Introduction to feature engineering techniques
- Distinctions between classification, regression, and clustering
- Overview of neural networks and their functions
- Introduction to model evaluation metrics (e.g., accuracy, F1 score, precision, and recall)
Learning resources:
- 📚 The Hundred-Page Machine Learning Book by Andriy Burkov
- 📚 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Geron Aurelien
- 🎥 Neural networks
- 📜 Top 6 Machine Learning Algorithms for Classification | Towards Data Science
- 📜 Evaluation Metrics in Machine Learning - GeeksforGeeks
- 📚 Designing Machine Learning Systems
Generative AI
What you should learn:
- Definition and scope of generative AI
- Capabilities for text, image, video, and speech generation
Learning resources:
Large Language Models (LLMs)
What you should learn:
- Definition and purpose of large language models
- Understanding transformer architecture
- Overview of various large language models available
- Guide to prompt engineering and its fundamental principles
Learning resources:
- 📚 AI Engineering
- 🎥 Deep Dive into LLMs like ChatGPT
- 🎥 Large Language Models explained briefly
- 📜 Prompt Engineering Best Practices: Tips, Tricks, and Tools | DigitalOcean
Local LLMs
What you should learn:
- Instructions for running Ollama locally
- Steps for downloading models
- Techniques for invoking models via Command-Line Interface (CLI)
- Guidance for invoking models through REST APIs
- Setting up Open WebUI and integrating it with Ollama
Learning resources:
GitHub Copilot
What you should learn:
- Setup procedures for integrating GitHub Copilot with Visual Studio Code
- Utilizing chat mode for code generation
- Exploring agentic mode capabilities for enhanced productivity
- Best practices for effective prompt design
- Understanding the limitations and ethical implications of AI-assisted coding
Learning resources:
- 📜 Getting started with GitHub Copilot
- 📜 Prompt engineering for GitHub Copilot Chat - GitHub Docs
- 📜 GitHub Copilot
Azure AI
What you should learn:
- Creating and managing projects within Azure AI Foundry
- Steps for deploying trained models
- Testing models using the playground feature
- Invoking models via REST APIs
- Adjusting invocation parameters for specific use cases
- Monitoring model performance with Azure tools
Learning resources:
Embeddings
What you should learn:
- Definition and significance of embeddings in AI
- Techniques for creating embeddings
- Understanding vector databases used for embedding storage
- Methods for querying embeddings effectively
- Techniques for improving embedding quality
Learning resources:
- 🎥 What are Word Embeddings?
- 📜 Embeddings | Machine Learning | Google for Developers
- 🛠️ GitHub - pgvector/pgvector: Open-source vector similarity search for Postgres
- 📜 OpenAI Platform
- 📜 Integrated Vector Database - Azure Cosmos DB
LangChain
What you should learn:
- Purpose and capabilities of LangChain
- Instructions for invoking models using the LangChain SDK
- Steps for creating and querying embeddings with LangChain SDK
- Building a chatbot leveraging LangChain functionalities
Learning resources:
- 📜 How-to guides | ️Langchain
- 📜 Build a simple LLM application with chat models and prompt templates | ️ Langchain
Retrieval - Augmented Generation (RAG)
What you should learn:
- Overview of RAG and its components
- Use cases for implementing RAG
- Workflow of RAG in practice
- Building a RAG system utilizing Azure AI Foundry
Learning resources:
- 🎥 RAG From Scratch
- 📜 Your Knowledge, Your AI: The Benefits of Running AI Locally
- 📜 Use your own data with Azure OpenAI in Azure AI Foundry Models - Azure OpenAI
- 📜 LangChain overview - Docs by LangChain
AI SDK
What you should learn:
- Creating UI for chatbot
- Generating text response
- Building chat prompt
- Rendering streaming response
Learning resources:
Agentic AI
What you should learn:
- Definition and role of agents in AI
- Exploration of agentic capabilities
- Practical use cases for different agent applications
Learning resources:
- 📜 Agentic AI Beyond the Hype: A Practical Perspective
- 📜 Agentic AI vs. Generative AI | IBM
- 🛠️ What is Langflow? | Langflow Documentation
- 🛠️ GitHub - microsoft/agent-framework: A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET.
Model Context Protocol (MCP)
What you should learn:
- Definition and meaning of MCP
- Availability of MCP tooling
- Steps for creating an MCP-driven API
- Integrating MCP-driven tools with Ollama
- Best practices for securing MCP APIs
Learning resources:
- 🎥 Claude’s Model Context Protocol is here… Let’s test it
- 📜 Streaming responses with tool calling· Ollama Blog
- 📜 What is the Model Context Protocol (MCP)? - Model Context Protocol
- 📜 Awesome MCP Servers
- 📜 Model Context Protocol (MCP) using Ollama
- 📜 Build an MCP server - Model Context Protocol
Agent2Agent (A2A) Protocol
What you should learn:
- Definition and meaning of A2A
- Core concepts: agent card, discovery, task lifecycle
Learning resources: