Learn Machine Learning with Python

What machine learning is, the end-to-end workflow, and splitting data to measure models honestly.

• Introduction to Machine Learning
• The Machine Learning Workflow
• Features, Labels and Train/Test Splits

Predicting numbers: linear regression, many features, regularisation and how to measure errors.

• Linear Regression
• Multiple and Polynomial Regression
• Regularisation: Ridge and Lasso
• Regression Metrics

Predicting categories with the most widely used classification algorithms, and measuring them properly.

• Logistic Regression
• Classification Metrics
• k-Nearest Neighbours
• Decision Trees
• Random Forests
• Gradient Boosting
• Support Vector Machines

Feature engineering, pipelines, cross-validation, tuning, imbalanced data and overfitting.

• Feature Engineering
• Pipelines and Column Transformers
• Cross-Validation
• Hyperparameter Tuning
• Overfitting and the Bias-Variance Trade-off
• Imbalanced Data

Finding structure without labels: clustering, dimensionality reduction and anomaly detection.

• k-Means Clustering
• Principal Component Analysis (PCA)
• Anomaly Detection

How neural networks learn, and building and training them with PyTorch.

• How Neural Networks Learn
• PyTorch Tensors and Autograd
• Training a Neural Network in PyTorch
• Image Classification with CNNs

Turning text into features, and building applications on top of large language models.

• Text Classification
• Embeddings and Semantic Search
• Using LLM APIs in Python

Saving models, serving predictions with an API, and tracking experiments and model health.

• Saving and Loading Models
• Serving a Model with an API
• MLOps: Models in Production

End-to-end machine learning projects from raw data to an evaluated model.

• Project: House Price Prediction
• Project: Customer Churn Prediction

Machine learning questions asked in data scientist and ML engineer interviews.

• Beginner Questions
• Intermediate Questions
• Advanced Questions