Machine Learning Tutorials
Master machine learning, data science, and analytics through interactive tutorials and hands-on projects
Neural Networks Fundamentals
Master the foundations of neural networks from mathematical principles to practical implementations. 8 comprehensive chapters covering feedforward networks, backpropagation, activation functions, CNNs, RNNs, and LSTMs.
Complete neural networks course with detailed explanations, formulas, and code examples covering feedforward networks, backpropagation, CNNs, RNNs, and LSTMs.
Transformer Architecture Deep Dive
Master the Transformer architecture that revolutionized NLP. 10 comprehensive chapters covering attention mechanisms, self-attention, multi-head attention, positional encoding, encoder-decoder architecture, and implementation details.
Deep dive into Transformer architecture with extensive formulas, code examples, and visual explanations covering attention, self-attention, multi-head attention, and complete implementation.
Large Language Models (LLMs)
Master Large Language Models from pre-training to fine-tuning. 8 comprehensive chapters covering BERT, GPT, transfer learning, fine-tuning strategies, prompt engineering, and practical applications.
Complete LLM course covering BERT, GPT, pre-training, fine-tuning, LoRA, prompt engineering, and practical applications with detailed explanations and code examples.
RAG & Retrieval Systems
Master Retrieval-Augmented Generation (RAG) systems from vector databases to production deployment. 7 comprehensive chapters covering embeddings, retrieval strategies, RAG architecture, and building production systems.
Complete RAG tutorial covering embeddings, vector databases, retrieval strategies, RAG architecture, and production deployment with detailed explanations and code examples.
Building Agentic AI Systems
Production Handbook: 22 chapters across 5 sections covering architectures, tool use, MCP, memory, multi-agent orchestration, LangGraph, safety, evaluation, fine-tuning, and frontier research. Prerequisite: Agentic AI Foundations.
Complete advanced course on building Agentic AI systems: agent architectures, MCP, memory systems, multi-agent orchestration, LangGraph, CrewAI, safety, evaluation, and fine-tuning for tool use.
Agentic AI Foundations
Core Concepts: 8 chapters covering the agent loop, ReAct framework, tool-using agents, multi-agent systems, orchestration, and building your first production agent. The recommended starting point before the advanced course.
Core Concepts: agent loop, ReAct, tool use, multi-agent systems, and orchestration across 8 focused chapters. Start here before the Production Handbook.