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Home Fundamentals Linux Cloud AI AI/ML Certifications Platform On-Premises

AI/ML Engineering

AI/ML Engineering

Prerequisites

Prerequisites Prerequisites & Environment Setup Home AI Workstation Fundamentals Reproducible Python, CUDA, and ROCm Environments Notebooks, Scripts, and Project Layouts

AI-Native Development

AI-Native Development Anthropic Agent SDK and Runtime Patterns AI Coding Tools Landscape Local Models for AI Coding Claude Code & CLI Deep Dive Agent-First IDEs CLI AI Coding Agents Prompt Engineering Fundamentals AI-Powered Code Generation AI-Assisted Debugging & Optimization Building with AI Coding Assistants

Generative AI

Generative AI Introduction to Large Language Models Tokenization & Text Processing Text Generation & Sampling Strategies Embeddings & Semantic Search Vector Space Visualization Reasoning Models: System 2 Thinking

Vector Search & RAG

Vector Search & RAG Vector Databases Deep Dive Building RAG Systems Advanced RAG Patterns RAG Evaluation & Optimization Long-Context LLMs and Prompt Caching Home-Scale RAG Systems

Frameworks & Agents

Frameworks & Agents LangChain Fundamentals LangChain Advanced LangGraph for Agents LlamaIndex Building AI Agents Agent Memory & Planning Multi-Agent Systems Model Context Protocol (MCP) for Agents Computer Use and Browser Automation Agents Next-Gen Agentic Frameworks

MLOps & LLMOps

MLOps & LLMOps ML DevOps Foundations Docker for ML CI/CD for ML Kubernetes for ML Advanced Kubernetes Experiment Tracking Data Pipelines ML Pipelines Model Serving ML Monitoring Notebooks to Production for ML/LLMs Small-Team Private AI Platform

AI Infrastructure

AI Infrastructure Cloud AI Services AIOps High-Performance LLM Inference: vLLM and sglang Local Inference Stack for Learners Home AI Operations and Cost Model GPU Memory Hierarchy and Bandwidth Math for LLM Inference Production-Tier LLM Inference Engines: Decision Framework Benchmarking LLM Inference: TTFT, TPOT, and Workload-Aware Load Shaping

Synthesis Apps

Synthesis Apps LLM-Native Stack: Inference and Memory on Kubernetes

Advanced GenAI & Safety

Advanced GenAI & Safety Fine-tuning LLMs LoRA & Parameter-Efficient Fine-tuning Diffusion Models RLHF & Alignment Advanced Generation Techniques LLM Evaluation AI Red Teaming AI Safety & Alignment Modern PEFT: DoRA and PiSSA Single-GPU Local Fine-Tuning Multi-GPU and Home-Lab Fine-Tuning Reasoning-Model RL: GRPO, RLVR, and DeepSeek-R1

Multimodal AI

Multimodal AI Voice & Audio AI Vision AI Video AI Multimodal-First AI Design

Deep Learning Foundations

Deep Learning Foundations Self-Supervised Learning Graph Neural Networks NumPy, Pandas & Data Tooling for ML PyTorch Fundamentals Training Neural Networks CNNs & Computer Vision RNNs & Sequence Models Backpropagation Deep Dive Backpropagation and Autograd from Scratch Modern Transformers: RoPE, ALiBi, and Attention-Head Geometry

Machine Learning

Machine Learning Scikit-learn API & Pipelines Linear & Logistic Regression with Regularization Model Evaluation, Validation, Leakage & Calibration Feature Engineering & Preprocessing Decision Trees & Random Forests XGBoost & Gradient Boosting Naive Bayes, k-NN & SVMs Unsupervised Learning: Clustering Anomaly Detection & Novelty Detection Dimensionality Reduction Hyperparameter Optimization Time Series Forecasting Class Imbalance & Cost-Sensitive Learning ML Interpretability + Failure Slicing Probabilistic & Bayesian ML with PyMC Recommender Systems Conformal Prediction & Uncertainty Quantification Fairness & Bias Auditing Causal Inference for ML Practitioners

Reinforcement Learning

Reinforcement Learning RL Practitioner Foundations Offline RL & Imitation Learning

Bridges to Other Tracks

AI/ML Engineering Bridges From AI Builder to AI Platform Engineer From Home Lab AI to Private AI Infrastructure

Appendix A: History of AI/ML

Appendix A: History of AI/ML History of AI & Machine Learning

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Prompt Engineering Fundamentals

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