Book Image

Building Machine Learning Systems with Python

Book Image

Building Machine Learning Systems with Python

Overview of this book

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.

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Building Machine Learning Systems with Python
Jul, 2013 | 290 pages
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Table of Contents (20 chapters)
Building Machine Learning Systems with Python
Building Machine Learning Systems with Python
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 – 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
2
Learning How to Classify with Real-world Examples
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
3
Clustering – Finding Related Posts
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
4
Topic Modeling
Topic Modeling
Latent Dirichlet allocation (LDA)
Comparing similarity in topic space
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 Naive Bayes classifier
Creating our first classifier and tuning it
Cleaning tweets
Taking the word types into account
Summary
7
Regression – Recommendations
Regression – Recommendations
Predicting house prices with regression
Penalized regression
P greater than N scenarios
Summary
8
Regression – Recommendations Improved
Regression – Recommendations Improved
Improved recommendations
Basket analysis
Summary
9
Classification III – Music Genre Classification
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
10
Computer Vision – Pattern Recognition
Computer Vision – Pattern Recognition
Introducing image processing
Loading and displaying images
Classifying a harder dataset
Local feature representations
Summary
11
Dimensionality Reduction
Dimensionality Reduction
Sketching our roadmap
Selecting features
Other feature selection methods
Feature extraction
Multidimensional scaling (MDS)
Summary
12
Big(ger) Data
Big(ger) Data
Learning about big data
Using jug to break up your pipeline into tasks
Using Amazon Web Services (AWS)
Summary
Where to Learn More about Machine Learning
Where to Learn More about Machine Learning
Online courses
Books
What was left out
Summary
Index
Index

Preprocessing – similarity measured as similar number of common words

As we have seen previously, the bag-of-word approach is both fast and robust. However, it is not without challenges. Let's dive directly into them.

Converting raw text into a bag-of-words

We do not have to write a custom code for counting words and representing those counts as a vector. Scikit's CountVectorizer does the job very efficiently. It also has a very convenient interface. Scikit's functions and classes are imported via the sklearn package as follows:

>>> from sklearn.feature_extraction.text import CountVectorizer
>>> vectorizer = CountVectorizer(min_df=1)

The parameter min_df determines how CountVectorizer treats words that are not used frequently (minimum document frequency). If it is set to an integer, all words occurring less than that value will be dropped. If it is a fraction, all words that occur less than that fraction of the overall dataset will be dropped. The parameter max_df works in...

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