Building Machine Learning Systems using Python

Preface

Introduction

• History of machine learning
• Classification of machine learning
• Challenges faced in adopting machine learning
• Applications
• Conclusion
• Questions

Linear Regression

• Linear regression in one variable
• Linear regression in multiple variables
• Gradient descent
• Polynomial regression
• Conclusion
• Questions

Classification Using Logistic Regression

• Introduction
• Binary classification
• Logistic regression
• Multiclass classification
• Conclusion
• Questions

Overfitting and Regularization

• Overfitting and regularization in linear regression
• Overfitting and regularization in logistic regression
• Conclusion
• Questions

Feasibility of Learning

• Introduction
• Feasibility of learning an unknown target function
• In-sample error and out-of-sample error
• Conclusion
• Questions

Support Vector Machine

• Introduction
• Margin and Large Margin methods
• Kernel methods
• Conclusion
• Questions

Neural Network

• Introduction
• Early models
• Perceptron learning
• Back propagation
• Stochastic Gradient Descent
• Conclusion
• Questions

Decision Trees

• Introduction
• Decision trees
• Regression trees
• Stopping criterion and pruning loss functions in decision trees
• Categorical attributes, multiway splits, and missing values in decision trees
• Instability in decision trees
• Conclusion
• Questions

Unsupervised Learning

• Introduction
• Clustering
• K-means clustering
• Principal Component Analysis (PCA)
• Conclusion
• Questions

Theory of Generalization

• Introduction
• Training versus testing
• Bounding the testing error
• VC dimension
• Conclusion
• Questions

Bias and Fairness in Machine Learning

• Introduction
• How to detect bias?
• How to fix biases or achieve fairness in ML?
• Conclusion
• Questions