Machine Learning with Python

Master NumPy arrays, vectorised operations, linear algebra, and random number generation for ML

Load, explore, clean, and transform datasets using Pandas DataFrames for ML preparation

Create insightful plots for exploratory data analysis — histograms, scatter plots, correlation heatmaps, and more

Scale features, encode categories, handle missing values, and engineer new features for better ML models

Understand and implement linear regression — the foundation of supervised learning — with gradient descent and scikit-learn

Build binary and multi-class classifiers using logistic regression, understand decision boundaries and probability outputs

Build interpretable decision trees and powerful ensemble models using Random Forests with feature importance analysis

Master SVMs for classification and regression — understand the kernel trick, hyperplanes, and margin maximisation

Implement KNN for classification and regression, understand distance metrics, and choose the optimal K using cross-validation

Discover hidden groups in unlabelled data using K-Means, choose optimal K with the elbow method, and visualise clusters

Reduce dimensionality while preserving variance — visualise high-dimensional data and speed up ML models with PCA

Build neural networks from scratch and with scikit-learn — understand layers, activation functions, backpropagation, and training

Properly evaluate ML models using cross-validation, learning curves, bias-variance tradeoff, and hyperparameter tuning

Master gradient boosting — the algorithm behind most Kaggle winners — and implement XGBoost for high-performance predictions

Process and classify text data — tokenisation, TF-IDF, sentiment analysis, and text classification with scikit-learn

Master L1/L2 regularisation, dropout, early stopping, and data augmentation to build models that generalise well

Analyse temporal data — trends, seasonality, ARIMA models, and forecasting with Python

Build production-ready ML pipelines with scikit-learn — automate preprocessing, feature engineering, and model training

Build a complete ML project from data exploration to deployment — apply everything you've learned in a real-world scenario