Designing Machine Learning Systems with Python

Design efficient machine learning systems that give you more accurate results

Designing Machine Learning Systems with Python

David Julian

1 customer reviews
Design efficient machine learning systems that give you more accurate results
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Book Details

ISBN 139781785882951
Paperback232 pages

Book Description

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.

Table of Contents

Chapter 1: Thinking in Machine Learning
The human interface
Design principles
Summary
Chapter 2: Tools and Techniques
Python for machine learning
IPython console
Installing the SciPy stack
NumPY
Matplotlib
Pandas
SciPy
Scikit-learn
Summary
Chapter 3: Turning Data into Information
What is data?
Big data
Signals
Cleaning data
Visualizing data
Summary
Chapter 4: Models – Learning from Information
Logical models
Tree models
Rule models
Summary
Chapter 5: Linear Models
Introducing least squares
Logistic regression
Multiclass classification
Regularization
Summary
Chapter 6: Neural Networks
Getting started with neural networks
Logistic units
Cost function
Implementing a neural network
Gradient checking
Other neural net architectures
Summary
Chapter 7: Features – How Algorithms See the World
Feature types
Operations and statistics
Structured features
Transforming features
Principle component analysis
Summary
Chapter 8: Learning with Ensembles
Ensemble types
Bagging
Boosting
Ensemble strategies
Summary
Chapter 9: Design Strategies and Case Studies
Evaluating model performance
Model selection
Learning curves
Real-world case studies
Machine learning at a glance
Summary

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

Authors

Table of Contents

Chapter 1: Thinking in Machine Learning
The human interface
Design principles
Summary
Chapter 2: Tools and Techniques
Python for machine learning
IPython console
Installing the SciPy stack
NumPY
Matplotlib
Pandas
SciPy
Scikit-learn
Summary
Chapter 3: Turning Data into Information
What is data?
Big data
Signals
Cleaning data
Visualizing data
Summary
Chapter 4: Models – Learning from Information
Logical models
Tree models
Rule models
Summary
Chapter 5: Linear Models
Introducing least squares
Logistic regression
Multiclass classification
Regularization
Summary
Chapter 6: Neural Networks
Getting started with neural networks
Logistic units
Cost function
Implementing a neural network
Gradient checking
Other neural net architectures
Summary
Chapter 7: Features – How Algorithms See the World
Feature types
Operations and statistics
Structured features
Transforming features
Principle component analysis
Summary
Chapter 8: Learning with Ensembles
Ensemble types
Bagging
Boosting
Ensemble strategies
Summary
Chapter 9: Design Strategies and Case Studies
Evaluating model performance
Model selection
Learning curves
Real-world case studies
Machine learning at a glance
Summary

Book Details

ISBN 139781785882951
Paperback232 pages
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