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AI/ML Engineering
AI/ML Engineering
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Phase 0: Prerequisites
Phase 0: Prerequisites
Prerequisites & Environment Setup
Phase 1: AI-Native Development
Phase 1: AI-Native Development
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
Phase 2: Generative AI
Phase 2: 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
Phase 3: Vector Search & RAG
Phase 3: Vector Search & RAG
Vector Databases Deep Dive
Building RAG Systems
Advanced RAG Patterns
RAG Evaluation & Optimization
Long-Context LLMs and Prompt Caching
Phase 4: Frameworks & Agents
Phase 4: 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
Phase 5: MLOps & LLMOps
Phase 5: 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
Phase 6: AI Infrastructure
Phase 6: AI Infrastructure
Cloud AI Services
AIOps
High-Performance LLM Inference: vLLM and sglang
Phase 7: Advanced GenAI & Safety
Phase 7: 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
Phase 8: Multimodal AI
Phase 8: Multimodal AI
Voice & Audio AI
Vision AI
Video AI
Multimodal-First AI Design
Phase 9: Deep Learning Foundations
Phase 9: Deep Learning Foundations
Neural Network Fundamentals
PyTorch Fundamentals
Training Neural Networks
CNNs & Computer Vision
RNNs & Sequence Models
Backpropagation Deep Dive
Transformers from Scratch
Phase 10: Classical ML
Phase 10: Classical ML
Scikit-learn & Classical ML
XGBoost & Gradient Boosting
Time Series Forecasting
Appendix A: History of AI/ML
Appendix A: History of AI/ML
History of AI & Machine Learning
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Phase 7: Advanced GenAI & Safety
AI/ML Engineering Track
| Phase 7
Modules
Section titled “Modules”
#
Module
1.1
Fine-tuning LLMs
1.2
LoRA & Parameter-Efficient Fine-tuning
1.3
Diffusion Models
1.4
RLHF & Alignment
1.5
Advanced Generation Techniques
1.6
LLM Evaluation
1.7
AI Red Teaming
1.8
AI Safety & Alignment
1.9
Modern PEFT: DoRA and PiSSA