Book Image

Designing Machine Learning Systems with Python

By : David Julian
Book Image

Designing Machine Learning Systems with Python

By: David Julian

Overview of this book

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.

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Designing Machine Learning Systems with Python
David Julian
Apr, 2016 | 232 pages
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Table of Contents (16 chapters)
Designing Machine Learning Systems with Python
Designing Machine Learning Systems with Python
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Thinking in Machine Learning
Thinking in Machine Learning
The human interface
Design principles
Summary
2
Tools and Techniques
Tools and Techniques
Python for machine learning
IPython console
Installing the SciPy stack
NumPY
Matplotlib
Pandas
SciPy
Scikit-learn
Summary
3
Turning Data into Information
Turning Data into Information
What is data?
Big data
Signals
Cleaning data
Visualizing data
Summary
4
Models – Learning from Information
Models – Learning from Information
Logical models
Tree models
Rule models
Summary
5
Linear Models
Linear Models
Introducing least squares
Logistic regression
Multiclass classification
Regularization
Summary
6
Neural Networks
Neural Networks
Getting started with neural networks
Logistic units
Cost function
Implementing a neural network
Gradient checking
Other neural net architectures
Summary
7
Features – How Algorithms See the World
Features – How Algorithms See the World
Feature types
Operations and statistics
Structured features
Transforming features
Principle component analysis
Summary
8
Learning with Ensembles
Learning with Ensembles
Ensemble types
Bagging
Boosting
Ensemble strategies
Summary
9
Design Strategies and Case Studies
Design Strategies and Case Studies
Evaluating model performance
Model selection
Learning curves
Real-world case studies
Machine learning at a glance
Summary
Index
Index

Scikit-learn

This includes algorithms for the most common machine learning tasks, such as classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.

Scikit-learn comes with several real-world data sets for us to practice with. Let's take a look at one of these—the Iris data set:

from sklearn import datasets
iris = datasets.load_iris()
iris_X = iris.data
iris_y = iris.target
iris_X.shape
(150, 4)

The data set contains 150 samples of three types of irises (Setosa, Versicolor, and Virginica), each with four features. We can get a description on the dataset:

iris.DESCR

We can see that the four attributes, or features, are sepal width, sepal length, petal length, and petal width in centimeters. Each sample is associated with one of three classes. Setosa, Versicolor, and Virginica. These are represented by 0, 1, and 2 respectively.

Let's look at a simple classification problem using this data. We want to predict the type of iris based on its features: the...

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