Lessons 1: Preface

• What this course covers
• What you need for this course
• Who this course is for
• Conventions

Lessons 2: Regression – The Workhorse of Data Science

• Regression analysis and data science
• Python for data science
• Python packages and functions for linear models

Lessons 3: Approaching Simple Linear Regression

• Defining a regression problem
• Starting from the basics
• Extending to linear regression
• Minimising the cost function

Lessons 4: Multiple Regression in Action

• Using multiple features
• Revisiting gradient descent
• Estimating feature importance
• Interaction models
• Polynomial regression

Lessons 5: Logistic Regression

• Defining a classification problem
• Defining a probability-based approach
• Revisiting gradient descent
• Multiclass Logistic Regression

Lessons 6: Data Preparation

• Numeric feature scaling
• Qualitative feature encoding
• Numeric feature transformation
• Missing data
• Outliers

Lessons 7: Achieving Generalisation

• Checking on out-of-sample data
• Greedy selection of features
• Regularisation optimized by grid-search
• Stability selection

Lessons 8: Online and Batch Learning

• Batch learning
• Online mini-batch learning

Lessons 9: Advanced Regression Methods

• Least Angle Regression
• Bayesian regression
• SGD classification with hinge loss
• Regression trees (CART)
• Bagging and boosting
• Gradient Boosting Regressor with LAD

Lessons 10: Real-world Applications for Regression Models

• Downloading the datasets
• A regression problem
• An imbalanced and multiclass classification problem
• A ranking problem
• A time series problem

Approaching Simple Linear Regression

Multiple Regression in Action

Logistic Regression

Data Preparation

Achieving Generalisation

Online and Batch Learning

Advanced Regression Methods