Designing Machine Learning Systems with Python

David Julian

• Publisher: Packt Publishing
• Year: 2016
• ISBN:
  9781785882951
• Edition: 1
• Topic: Ciencia de la computación
• Subtopic: Programación en Python

Design efficient machine learning systems that give you more accurate results

About This Book

• Gain an understanding of the machine learning design process
• Optimize machine learning systems for improved accuracy
• Understand common programming tools and techniques for machine learning
• Develop techniques and strategies for dealing with large amounts of data from a variety of sources
• Build models to solve unique tasks

Who This Book Is For

This book is for data scientists, scientists, or just the curious. To get the most out of this book, you will need to know some linear algebra and some Python, and have a basic knowledge of machine learning concepts.

What You Will Learn

• Gain an understanding of the machine learning design process
• Optimize the error function of your machine learning system
• Understand the common programming patterns used in machine learning
• Discover optimizing techniques that will help you get the most from your data
• Find out how to design models uniquely suited to your task

In Detail

Machine learning is one of the fastest growing trends in modern computing. It has applications in a wide range of fields, including economics, the natural sciences, web development, and business modeling. In order to harness the power of these systems, it is essential that the practitioner develops a solid understanding of the underlying design principles.

There are many reasons why machine learning models may not give accurate results. By looking at these systems from a design perspective, we gain a deeper understanding of the underlying algorithms and the optimisational methods that are available. This book will give you a solid foundation in the machine learning design process, and enable you to build customised machine learning models to solve unique problems. You may already know about, or have worked with, some of the off-the-shelf machine learning models for solving common problems such as spam detection or movie classification, but to begin solving more complex problems, it is important to adapt these models to your own specific needs. This book will give you this understanding and more.

Style and approach

This easy-to-follow, step-by-step guide covers the most important machine learning models and techniques from a design perspective.

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

eBooks, discount offers, and more

Why subscribe?

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

Designing Machine Learning Systems with Python

Copyright © 2016 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

First published: April 2016

Production reference: 1310316

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-78588-295-1

www.packtpub.com

Credits

Author

David Julian

Reviewer

Dr. Vahid Mirjalili

Commissioning Editor

Veena Pagare

Acquisition Editor

Tushar Gupta

Content Development Editor

Merint Thomas Mathew

Technical Editor

Abhishek R. Kotian

Copy Editor

Angad Singh

Project Coordinator

Suzanne Coutinho

Proofreader

Safis Editing

Indexer

Rekha Nair

Graphics

Disha Haria

Jason Monteiro

Production Coordinator

Aparna Bhagat

Cover Work

Aparna Bhagat

About the Author

David Julian is currently working on a machine learning project with Urban Ecological Systems Ltd and Blue Smart Farms (http://www.bluesmartfarms.com.au) to detect and predict insect infestation in greenhouse crops. He is currently collecting a labeled training set that includes images and environmental data (temperature, humidity, soil moisture, and pH), linking this data to observations of infestation (the target variable), and using it to train neural net models. The aim is to create a model that will reduce the need for direct observation, be able to anticipate insect outbreaks, and subsequently control conditions. There is a brief outline of the project at http://davejulian.net/projects/ues. David also works as a data analyst, I.T. consultant, and trainer.

I would like to thank Hogan Gleeson, James Fuller, Kali McLaughlin and Nadine Miller. This book would not have been possible without the great work of the open source machine learning community.

About the Reviewer

Dr. Vahid Mirjalili is a data scientist with a diverse background in engineering, mathematics, and computer science. With his specialty in data mining, he is very interested in predictive modeling and getting insights from data. Currently, he is working towards publishing a book on big data analysis, which covers a wide range of tools and techniques for analyzing massive data sets. Furthermore, as a Python developer, he likes to contribute to the open source community. He has developed Python packages for data clustering, such as PyClust. A collection of...

Table of contents

• Designing Machine Learning Systems with Python