PacktLib: Building Machine Learning Systems with Python

Building Machine Learning Systems with Python


About the Authors

About the Reviewers


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


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


Clustering – Finding Related Posts

Measuring the relatedness of posts

Preprocessing – similarity measured as similar number of common words


Solving our initial challenge

Tweaking the parameters


Topic Modeling

Latent Dirichlet allocation (LDA)

Comparing similarity in topic space

Choosing the number of topics


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!


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


Regression – Recommendations

Predicting house prices with regression

Penalized regression

P greater than N scenarios


Regression – Recommendations Improved

Improved recommendations

Basket analysis


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


Computer Vision – Pattern Recognition

Introducing image processing

Loading and displaying images

Classifying a harder dataset

Local feature representations


Dimensionality Reduction

Sketching our roadmap

Selecting features

Other feature selection methods

Feature extraction

Multidimensional scaling (MDS)


Big(ger) Data

Learning about big data

Using jug to break up your pipeline into tasks

Using Amazon Web Services (AWS)


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