AI/ML Fundamentals – Complete Handwritten Revision Notes

What you will learn

• ✓Understand the AI landscape and differences between AI, ML, Deep Learning and Generative AI.
• ✓Learn supervised, unsupervised and reinforcement learning.
• ✓Understand data preprocessing, feature engineering and data leakage.
• ✓Learn how models train using loss functions and gradient descent.
• ✓Explore Linear Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, K-Means and PCA.
• ✓Understand underfitting, overfitting, generalization and regularization.
• ✓Learn training, validation, testing and cross-validation.
• ✓Evaluate models using confusion matrix, precision, recall, F1-score, MAE, MSE and RMSE.
• ✓Understand probability, statistical distributions and bias-variance trade-off.
• ✓Learn neural network basics, activation functions and backpropagation.
• ✓Understand LLM fundamentals, tokenization, inference and generation controls.
• ✓Learn embeddings, vector representations and semantic similarity.
• ✓Understand AI Engineering workflows, RAG, tools and agents.
• ✓Learn production evaluation, security, monitoring, latency and cost.
• ✓Revise key concepts, formulas and practical AI/ML workflows.