Building Machine Learning Systems with Python

Expand your Python knowledge and learn all about machine-learning libraries in this user-friendly manual. ML is the next big breakthrough in technology and this book will give you the head-start you need.

Building Machine Learning Systems with Python

Progressing
Willi Richert, Luis Pedro Coelho

Expand your Python knowledge and learn all about machine-learning libraries in this user-friendly manual. ML is the next big breakthrough in technology and this book will give you the head-start you need.
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Book Details

ISBN 139781782161400
Paperback290 pages

About This Book

  • Master Machine Learning using a broad set of Python libraries and start building your own Python-based ML systems
  • Covers classification, regression, feature engineering, and much more guided by practical examples
  • A scenario-based tutorial to get into the right mind-set of a machine learner (data exploration) and successfully implement this in your new or existing projects

Who This Book Is For

This book is for Python programmers who are beginners in machine learning, but want to learn Machine learning. Readers are expected to know Python and be able to install and use open-source libraries. They are not expected to know machine learning, although the book can also serve as an introduction to some Python libraries for readers who know machine learning. This book does not go into the detail of the mathematics behind the algorithms.

This book primarily targets Python developers who want to learn and build machine learning in their projects, or who want to provide machine learning support to their existing projects, and see them getting implemented effectively.

Table of Contents

Chapter 1: Getting Started with Python Machine Learning
Machine learning and Python – the 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) machine learning application
Summary
Chapter 2: Learning How to Classify with Real-world Examples
The Iris dataset
Building more complex classifiers
A more complex dataset and a more complex classifier
Binary and multiclass classification
Summary
Chapter 3: Clustering – Finding Related Posts
Measuring the relatedness of posts
Preprocessing – similarity measured as similar number of common words
Clustering
Solving our initial challenge
Tweaking the parameters
Summary
Chapter 4: Topic Modeling
Latent Dirichlet allocation (LDA)
Comparing similarity in topic space
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 Naive Bayes classifier
Creating our first classifier and tuning it
Cleaning tweets
Taking the word types into account
Summary
Chapter 7: Regression – Recommendations
Predicting house prices with regression
Penalized regression
P greater than N scenarios
Summary
Chapter 8: Regression – Recommendations Improved
Improved recommendations
Basket analysis
Summary
Chapter 9: Classification III – 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 – Pattern Recognition
Introducing image processing
Loading and displaying images
Classifying a harder dataset
Local feature representations
Summary
Chapter 11: Dimensionality Reduction
Sketching our roadmap
Selecting features
Other feature selection methods
Feature extraction
Multidimensional scaling (MDS)
Summary
Chapter 12: Big(ger) Data
Learning about big data
Using jug to break up your pipeline into tasks
Using Amazon Web Services (AWS)
Summary

What You Will Learn

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

In Detail

Machine learning, the field of building systems that learn from data, is exploding on the Web and elsewhere. Python is a wonderful language in which to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation and an increasing number of machine learning libraries are developed for Python.

Building Machine Learning system with Python shows you exactly how to find patterns through raw data. The book starts by brushing up on your Python ML knowledge and introducing libraries, and then moves on to more serious projects on datasets, Modelling, Recommendations, improving recommendations through examples and sailing through sound and image processing in detail.

Using open-source tools and libraries, readers will learn how to apply methods to text, images, and sounds. You will also learn how to evaluate, compare, and choose machine learning techniques.

Written for Python programmers, Building Machine Learning Systems with Python teaches you how to use open-source libraries to solve real problems with machine learning. The book is based on real-world examples that the user can build on.

Readers will learn how to write programs that classify the quality of StackOverflow answers or whether a music file is Jazz or Metal. They will learn regression, which is demonstrated on how to recommend movies to users. Advanced topics such as topic modeling (finding a text’s most important topics), basket analysis, and cloud computing are covered as well as many other interesting aspects.

Building Machine Learning Systems with Python will give you the tools and understanding required to build your own systems, which are tailored to solve your problems.

Authors

Table of Contents

Chapter 1: Getting Started with Python Machine Learning
Machine learning and Python – the 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) machine learning application
Summary
Chapter 2: Learning How to Classify with Real-world Examples
The Iris dataset
Building more complex classifiers
A more complex dataset and a more complex classifier
Binary and multiclass classification
Summary
Chapter 3: Clustering – Finding Related Posts
Measuring the relatedness of posts
Preprocessing – similarity measured as similar number of common words
Clustering
Solving our initial challenge
Tweaking the parameters
Summary
Chapter 4: Topic Modeling
Latent Dirichlet allocation (LDA)
Comparing similarity in topic space
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 Naive Bayes classifier
Creating our first classifier and tuning it
Cleaning tweets
Taking the word types into account
Summary
Chapter 7: Regression – Recommendations
Predicting house prices with regression
Penalized regression
P greater than N scenarios
Summary
Chapter 8: Regression – Recommendations Improved
Improved recommendations
Basket analysis
Summary
Chapter 9: Classification III – 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 – Pattern Recognition
Introducing image processing
Loading and displaying images
Classifying a harder dataset
Local feature representations
Summary
Chapter 11: Dimensionality Reduction
Sketching our roadmap
Selecting features
Other feature selection methods
Feature extraction
Multidimensional scaling (MDS)
Summary
Chapter 12: Big(ger) Data
Learning about big data
Using jug to break up your pipeline into tasks
Using Amazon Web Services (AWS)
Summary

Book Details

ISBN 139781782161400
Paperback290 pages
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