📄️Math Foundations for ML
The linear algebra, calculus and gradient descent ideas behind machine learning, with NumPy examples you can run.
📄️Statistics and Probability for Data Science
Core statistics for data science: descriptive measures, distributions, sampling, hypothesis testing, confidence intervals and Bayes theorem.
📄️Data Visualization with Matplotlib and Seaborn
Create clear charts with Matplotlib and Seaborn: choosing the right plot, labelling, comparing distributions and avoiding misleading visuals.
📄️Decision Trees and Ensemble Methods
How decision trees work and why random forests and gradient boosting are the strongest default models for tabular data.
📄️Deep Learning with PyTorch
Train a neural network end to end with PyTorch: tensors, datasets, the training loop, evaluation, a small CNN and regularisation tips.
📄️Natural Language Processing Basics
Introduction to NLP: text preprocessing, bag of words and TF-IDF, word embeddings, transformers and a quick text classification example.
📄️Machine Learning in Production
Move models from notebooks to production: reproducibility, saving models, serving an API, monitoring data drift and retraining.
📄️End-to-End ML Project Walkthrough
Walk through a complete machine learning project: problem framing, data, baseline, model improvement, evaluation, explanation and delivery.