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AI/ML Foundations
Classical Machine Learning
Deep Learning
LLMs & Generative AI
Applied AI Engineering
Production ML & MLOps
Responsible AI
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AI/ML Foundations
Classical Machine Learning
Deep Learning
LLMs & Generative AI
Applied AI Engineering
Production ML & MLOps
Responsible AI
AI vs ML vs Deep Learning vs Generative AI
Supervised, Unsupervised & Reinforcement Learning
Training, Validation & Test Sets
Loss Functions & Optimization
Overfitting & Generalization
Metrics & Evaluation
Linear & Logistic Regression
Decision Trees, Random Forests & Gradient Boosting
Clustering & Dimensionality Reduction
Feature Engineering
Model Selection & Validation
Neural Network Basics
Backpropagation & Optimizers
Regularization for Deep Networks
Embeddings & Representation Learning
CNNs, RNNs & Transformers
Tokenization & Context Windows
Transformer Architecture
Pretraining, Fine-Tuning & Instruction Tuning
Prompting & Decoding
Retrieval-Augmented Generation (RAG)
Tool Calling & Agents
When to Use AI vs Normal Code
Structured Outputs & Validation
Retrieval, Search & Reranking
LLM Evaluation
Cost, Latency & Reliability
ML System Lifecycle
Training-Serving Skew
Model Serving & Inference
Monitoring & Drift
Experimentation & Rollouts
Fairness & Bias
Privacy & Data Governance
Interpretability & Explainability
Prompt Injection & AI Security
Human Review & Release Checklists