Book Image

Building Machine Learning Systems with Python - Second Edition

By : Luis Pedro Coelho, Willi Richert
Book Image

Building Machine Learning Systems with Python - Second Edition

By: Luis Pedro Coelho, Willi Richert

Overview of this book

<p>Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python is a wonderful language to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation. With its excellent collection of open source machine learning libraries you can focus on the task at hand while being able to quickly try out many ideas.</p> <p>This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and introducing libraries. You’ll quickly get to grips with serious, real-world projects on datasets, using modeling, creating recommendation systems. Later on, the book covers advanced topics such as topic modeling, basket analysis, and cloud computing. These will extend your abilities and enable you to create large complex systems.</p> <p>With this book, you gain the tools and understanding required to build your own systems, tailored to solve your real-world data analysis problems.</p>

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Building Machine Learning Systems with Python - Second Edition
Luis Pedro Coelho | Willi Richert
Mar, 2015 | 326 pages
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Table of Contents (20 chapters)
Building Machine Learning Systems with Python Second Edition
Building Machine Learning Systems with Python Second Edition
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Getting Started with Python Machine Learning
Getting Started with Python Machine Learning
Machine learning and Python – a dream team
What the book will teach you (and what it will not)
What to do when you are stuck
Getting started
Our first (tiny) application of machine learning
Summary
2
Classifying with Real-world Examples
Classifying with Real-world Examples
The Iris dataset
Building more complex classifiers
A more complex dataset and a more complex classifier
Classifying with scikit-learn
Binary and multiclass classification
Summary
3
Clustering – Finding Related Posts
Clustering – Finding Related Posts
Measuring the relatedness of posts
Preprocessing – similarity measured as a similar number of common words
Clustering
Solving our initial challenge
Tweaking the parameters
Summary
4
Topic Modeling
Topic Modeling
Latent Dirichlet allocation
Comparing documents by topics
Choosing the number of topics
Summary
5
Classification – Detecting Poor Answers
Classification – Detecting Poor Answers
Sketching our roadmap
Learning to classify classy answers
Fetching the data
Creating our first classifier
Deciding how to improve
Using logistic regression
Looking behind accuracy – precision and recall
Slimming the classifier
Ship it!
Summary
6
Classification II – Sentiment Analysis
Classification II – Sentiment Analysis
Sketching our roadmap
Fetching the Twitter data
Introducing the Naïve Bayes classifier
Creating our first classifier and tuning it
Cleaning tweets
Taking the word types into account
Summary
7
Regression
Regression
Predicting house prices with regression
Penalized or regularized regression
Summary
8
Recommendations
Recommendations
Rating predictions and recommendations
Basket analysis
Summary
9
Classification – Music Genre Classification
Classification – Music Genre Classification
Sketching our roadmap
Fetching the music data
Looking at music
Using FFT to build our first classifier
Improving classification performance with Mel Frequency Cepstral Coefficients
Summary
10
Computer Vision
Computer Vision
Introducing image processing
Local feature representations
Summary
11
Dimensionality Reduction
Dimensionality Reduction
Sketching our roadmap
Selecting features
Feature extraction
Multidimensional scaling
Summary
12
Bigger Data
Bigger Data
Learning about big data
Using Amazon Web Services
Summary
Where to Learn More Machine Learning
Where to Learn More Machine Learning
Online courses
Books
All that was left out
Summary
Index
Index

Creating our first classifier and tuning it

The Naïve Bayes classifiers resides in the sklearn.naive_bayes package. There are different kinds of Naïve Bayes classifiers:

  • GaussianNB: This classifier assumes the features to be normally distributed (Gaussian). One use case for it could be the classification of sex given the height and width of a person. In our case, we are given tweet texts from which we extract word counts. These are clearly not Gaussian distributed.

  • MultinomialNB: This classifier assumes the features to be occurrence counts, which is our case going forward, since we will be using word counts in the tweets as features. In practice, this classifier also works well with TF-IDF vectors.

  • BernoulliNB: This classifier is similar to MultinomialNB, but more suited when using binary word occurrences and not word counts.

As we will mainly look at the word occurrences, for our purpose the MultinomialNB classifier is best suited.

Solving an easy problem first

As we have seen, when we looked...

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