Machine learning- Full Course

Machine Learning — Blog Series Contents
PART 0: Before ML (Mindset & Big Picture)

• What is Machine Learning? (Without buzzwords)
• ML vs AI vs DL vs Statistics
• Why ML models fail in the real world
• The ML lifecycle: Data → Model → Deployment → Decay
• When NOT to use Machine Learning

PART 1: Mathematical Foundations (Intuition First)

• (No heavy proofs initially — geometry + visuals)
• Linear Algebra for ML
• Vectors as points, directions, and features
• Dot product = similarity (why cosine works)
• Matrix multiplication as a transformation
• Eigenvectors as “stable directions”
• Why high-dimensional space is weird
• Probability & Statistics
• Random variables as uncertainty containers
• Expectation as long-term behaviour
• Variance, bias, and noise (real meaning)
• Bayes theorem without formulas
• Maximum Likelihood vs MAP
• Optimization Basics
• Loss functions: measuring regret
• Gradient descent geometrically
• Local minima, saddle points, flat regions
• Learning rate as step-size in physics
• Convex vs non-convex problems

PART 2: Classical Machine Learning (Core)

• Supervised Learning
• Linear Regression from scratch
• Overfitting vs underfitting (bias–variance tradeoff)
• Regularization: L1, L2, Elastic Net
• Logistic Regression as a probabilistic model
• Decision Trees: splitting chaos into order
• Random Forests: wisdom of crowds
• Gradient Boosting intuitively
• XGBoost explained simply
• Model Evaluation
• Train/Validation/Test split myths
• Accuracy is a lie (precision, recall, F1)
• ROC vs PR curves
• Cross-validation done right
• Data leakage horror stories

PART 3: Unsupervised Learning

• Clustering as structure discovery
• K-Means geometrical intuition
• Hierarchical clustering
• DBSCAN and density-based thinking
• Dimensionality reduction vs feature selection
• PCA as variance maximisation
• When PCA amplifies bias (fairness angle)

PART 4: Feature Engineering (Underrated Superpower)

• Why features matter more than models
• Encoding categorical variables
• Scaling and normalisation myths
• Feature interactions
• Time-based features
• Feature leakage patterns
• Domain-driven feature design

PART 5: Neural Networks (Deep Learning)
Basics

• Perceptron: the neuron myth
• Why linear models fail
• Activation functions geometrically
• Backpropagation explained visually
• Vanishing & exploding gradients
• Architectures
• Fully connected networks
• CNNs: local connectivity intuition
• Pooling: information compression
• RNNs and sequence memory
• LSTM & GRU demystified
• Transformers at a high level

PART 6: Training Deep Models

• Initialization matters more than you think
• Batch vs mini-batch vs stochastic GD
• Optimizers: SGD, Adam, RMSProp
• Regularization in deep learning
• Dropout as ensemble trick
• BatchNorm explained
• Early stopping intuition

PART 7: Model Interpretability & Fairness

• Black-box vs glass-box models
• Feature importance myths
• SHAP and LIME intuitively
• Fairness in ML: what does it mean?
• Bias in data vs bias in models
• Fair PCA and representation learning
• Trade-offs: accuracy vs fairness

PART 8: ML Systems & Production

• Training vs inference pipelines
• Offline vs online learning
• Model versioning
• Data drift vs concept drift
• Monitoring ML in production
• Retraining strategies
• ML technical debt

PART 9: Applied Machine Learning

• ML for recommendation systems
• ML in search engines
• ML for fraud detection
• ML in healthcare (risks & ethics)
• ML in finance
• ML in sports analytics
• ML for NLP tasks
• ML for computer vision

PART 10: Research Thinking in ML

• How to read ML research papers
• Empirical vs theoretical papers
• Reproducibility crisis in ML
• Baselines nobody respects
• Ablation studies explained
• Writing a good ML paper
• Common research mistakes

PART 11: Advanced & Emerging Topics

• Self-supervised learning
• Contrastive learning
• Representation learning
• Meta-learning
• Online learning
• Causal ML
• Reinforcement Learning intuition
• LLMs and foundation models
• ML alignment & safety

PART 12: ML Career & Learning Path

• How to learn ML without drowning
• Math vs intuition — what to prioritise?
• ML interviews vs real ML
• Building impactful ML projects
• From engineer to ML researcher
• How to choose a research problem