PacktLib: Building Machine Learning Systems with Python

Building Machine Learning Systems with Python

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Preface

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

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

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

Topic Modeling

Latent Dirichlet allocation (LDA)

Comparing similarity in topic space

Choosing the number of topics

Summary

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

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

Regression – Recommendations

Predicting house prices with regression

Penalized regression

P greater than N scenarios

Summary

Regression – Recommendations Improved

Improved recommendations

Basket analysis

Summary

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

Computer Vision – Pattern Recognition

Introducing image processing

Loading and displaying images

Classifying a harder dataset

Local feature representations

Summary

Dimensionality Reduction

Sketching our roadmap

Selecting features

Other feature selection methods

Feature extraction

Multidimensional scaling (MDS)

Summary

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

Where to Learn More about Machine Learning

Where to Learn More about Machine Learning

Where to Learn More about Machine Learning

Index