Building Machine Learning Systems with Python - Second Edition

Get more from your data with the power of Python machine learning systems
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Building Machine Learning Systems with Python - Second Edition

Luis Pedro Coelho, Willi Richert

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Get more from your data with the power of Python machine learning systems
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Book Details

ISBN 139781784392772
Paperback326 pages

Book Description

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.

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.

With this book, you gain the tools and understanding required to build your own systems, tailored to solve your real-world data analysis problems.

Table of Contents

Chapter 1: 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
Chapter 2: 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
Chapter 3: 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
Chapter 4: Topic Modeling
Latent Dirichlet allocation
Comparing documents by topics
Choosing the number of topics
Summary
Chapter 5: 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
Chapter 6: 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
Chapter 7: Regression
Predicting house prices with regression
Penalized or regularized regression
Summary
Chapter 8: Recommendations
Rating predictions and recommendations
Basket analysis
Summary
Chapter 9: 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
Chapter 10: Computer Vision
Introducing image processing
Local feature representations
Summary
Chapter 11: Dimensionality Reduction
Sketching our roadmap
Selecting features
Feature extraction
Multidimensional scaling
Summary
Chapter 12: Bigger Data
Learning about big data
Using Amazon Web Services
Summary

What You Will Learn

  • Build a classification system that can be applied to text, images, or sounds
  • Use NumPy, SciPy, scikit-learn – scientific Python open source libraries for scientific computing and machine learning
  • Explore the mahotas library for image processing and computer vision
  • Build a topic model for the whole of Wikipedia
  • Employ Amazon Web Services to run analysis on the cloud
  • Debug machine learning problems
  • Get to grips with recommendations using basket analysis
  • Recommend products to users based on past purchases

Authors

Table of Contents

Chapter 1: 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
Chapter 2: 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
Chapter 3: 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
Chapter 4: Topic Modeling
Latent Dirichlet allocation
Comparing documents by topics
Choosing the number of topics
Summary
Chapter 5: 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
Chapter 6: 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
Chapter 7: Regression
Predicting house prices with regression
Penalized or regularized regression
Summary
Chapter 8: Recommendations
Rating predictions and recommendations
Basket analysis
Summary
Chapter 9: 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
Chapter 10: Computer Vision
Introducing image processing
Local feature representations
Summary
Chapter 11: Dimensionality Reduction
Sketching our roadmap
Selecting features
Feature extraction
Multidimensional scaling
Summary
Chapter 12: Bigger Data
Learning about big data
Using Amazon Web Services
Summary

Book Details

ISBN 139781784392772
Paperback326 pages
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From 1 reviews

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