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Designing Machine Learning Systems with Python电子书

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30人正在读 | 0人评论 9.8

作       者:David Julian

出  版  社:Packt Publishing

出版时间:2016-04-01

字       数:213.5万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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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.
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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

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