Module 01
Machine Learning
01
What Is Machine Learning
Understand the three types of machine learning, the fit-predict workflow, and when to choose ML over rule-based code.
02
Bias and Variance
Learn why models underfit or overfit, how model complexity controls the bias-variance tradeoff, and how to recognise each from training and test scores.
03
Gradient Descent
See how gradient descent minimises a loss function by taking small steps in the direction of steepest decrease, and how scikit-learn uses it inside SGDRegressor.
04
Regularization
Add L1 or L2 penalties to prevent overfitting, understand how Ridge keeps all features and Lasso zeros some out, and tune the regularization strength.
05
Support Vector Machines
Understand how SVMs find the widest margin between classes, how the C parameter balances margin and misclassification, and how kernels extend SVMs to non-linear problems.
06
Naive Bayes
Apply Bayes' theorem to classification, understand the naive independence assumption, and use GaussianNB for continuous features.
07
Gradient Boosting
Build an ensemble of trees that each correct the previous one's residuals, control the speed-accuracy tradeoff with learning_rate and n_estimators, and read feature importances.
08
Dimensionality Reduction
Use PCA to summarise many correlated features into a smaller set of uncorrelated principal components, read the explained variance ratio, and combine PCA with a classifier.
09
Feature Engineering
Create polynomial terms, interaction features, and binned categories from existing columns to give models richer inputs and improve accuracy.
10
Imbalanced Data
Handle rare-class problems where accuracy misleads, apply class_weight='balanced', and evaluate with precision, recall, and F1 instead of accuracy.
11
Learning Curves
Plot training and validation scores against training set size to diagnose whether your model suffers from high bias, high variance, or neither, and decide whether more data will help.
12
Ensemble Methods
Combine multiple models with bagging, voting, and stacking to reduce variance and improve accuracy beyond any single estimator.
13
Model Selection
Compare multiple algorithms fairly using cross-validation, understand the no-free-lunch theorem, and choose the right model for linear, non-linear, and interpretable problems.
14
ML Workflow
Structure an end-to-end machine learning project from problem definition through data exploration, feature engineering, model selection, evaluation, and iteration.
15
Model Monitoring
Detect data drift and concept drift after deployment, monitor prediction distributions over time, and know when to retrain.