Designing Machine Learning Systems With Python by Julian, David -- Read -- Imperial Library of Trantor

Index

Designing Machine Learning Systems with Python

Table of Contents Designing Machine Learning Systems with Python Credits About the Author About the Reviewer www.PacktPub.com

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Preface

What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support

Downloading the example code Errata Piracy Questions

1. Thinking in Machine Learning

The human interface Design principles

Types of questions Are you asking the right question? Tasks

Classification Regression Clustering Dimensionality reduction Errors Optimization Linear programming Models

Geometric models Probabilistic models Logical models

Features

Unified modeling language

Class diagrams Object diagrams Activity diagrams State diagrams

Summary

2. Tools and Techniques

Python for machine learning IPython console Installing the SciPy stack NumPY

Constructing and transforming arrays Mathematical operations

Matplotlib Pandas SciPy Scikit-learn Summary

3. Turning Data into Information

What is data? Big data

Challenges of big data

Data volume Data velocity Data variety

Data models Data distributions Data from databases Data from the Web Data from natural language Data from images Data from application programming interfaces

Signals

Data from sound

Cleaning data Visualizing data Summary

4. Models – Learning from Information

Logical models

Generality ordering Version space Coverage space PAC learning and computational complexity

Tree models

Purity

Rule models

The ordered list approach Set-based rule models

Summary

5. Linear Models

Introducing least squares

Gradient descent The normal equation

Logistic regression

The Cost function for logistic regression

Multiclass classification Regularization Summary

6. Neural Networks

Getting started with neural networks Logistic units Cost function

Minimizing the cost function

Implementing a neural network Gradient checking Other neural net architectures Summary

7. Features – How Algorithms See the World

Feature types

Quantitative features Ordinal features Categorical features

Operations and statistics Structured features Transforming features

Discretization Normalization Calibration

Principle component analysis Summary

8. Learning with Ensembles

Ensemble types Bagging

Random forests Extra trees

Boosting

Adaboost Gradient boosting

Ensemble strategies

Other methods

Summary

9. Design Strategies and Case Studies

Evaluating model performance Model selection

Gridsearch

Learning curves Real-world case studies

Building a recommender system

Content-based filtering Collaborative filtering Reviewing the case study

Insect detection in greenhouses

Reviewing the case study

Machine learning at a glance Summary

Index

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