book

Advanced Machine Learning with Python

Name: Advanced Machine Learning with Python
Author: John Hearty
ISBN: 9781784398637

by John Hearty

July 2016

Intermediate to advanced

278 pages

6h 48m

English

Packt Publishing

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Advanced Machine Learning with Python
Table of Contents
Advanced Machine Learning with Python
Credits
About the Author
About the Reviewers
www.PacktPub.com
eBooks, discount offers, and moreWhy subscribe?
Preface
What is advanced machine learning?
What should you expect from this book?
What this book covers

What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example codeDownloading the color images of this bookErrataPiracyQuestions
1. Unsupervised Machine Learning
Principal component analysisPCA – a primerEmploying PCA
Introducing k-means clustering
Clustering – a primerKick-starting clustering analysisTuning your clustering configurations
Self-organizing maps
SOM – a primerEmploying SOM
Further reading
Summary
2. Deep Belief Networks
Neural networks – a primerThe composition of a neural networkNetwork topologies
Restricted Boltzmann Machine
Introducing the RBMTopologyTrainingApplications of the RBMFurther applications of the RBM
Deep belief networks
Training a DBNApplying the DBNValidating the DBN
Further reading
Summary
3. Stacked Denoising Autoencoders
AutoencodersIntroducing the autoencoderTopologyTrainingDenoising autoencodersApplying a dA
Stacked Denoising Autoencoders
Applying the SdAAssessing SdA performance
Further reading
Summary
4. Convolutional Neural Networks
Introducing the CNNUnderstanding the convnet topologyUnderstanding convolution layersUnderstanding pooling layersTraining a convnetPutting it all togetherApplying a CNN
Further Reading
Summary
5. Semi-Supervised Learning
Introduction
Understanding semi-supervised learning
Semi-supervised algorithms in action
Self-trainingImplementing self-trainingFinessing your self-training implementationImproving the selection processContrastive Pessimistic Likelihood Estimation
Further reading
Summary
6. Text Feature Engineering
Introduction
Text feature engineering
Cleaning text dataText cleaning with BeautifulSoupManaging punctuation and tokenizingTagging and categorising wordsTagging with NLTKSequential taggingBackoff taggingCreating features from text dataStemmingBagging and random forestsTesting our prepared data
Further reading
Summary
7. Feature Engineering Part II
Introduction
Creating a feature set
Engineering features for ML applicationsUsing rescaling techniques to improve the learnability of featuresCreating effective derived variablesReinterpreting non-numeric featuresUsing feature selection techniquesPerforming feature selectionCorrelationLASSORecursive Feature EliminationGenetic models
Feature engineering in practice
Acquiring data via RESTful APIsTesting the performance of our modelTwitterTranslink TwitterConsumer commentsThe Bing Traffic APIDeriving and selecting variables using feature engineering techniquesThe weather API
Further reading
Summary
8. Ensemble Methods
Introducing ensemblesUnderstanding averaging ensemblesUsing bagging algorithmsUsing random forestsApplying boosting methodsUsing XGBoostUsing stacking ensemblesApplying ensembles in practice
Using models in dynamic applications
Understanding model robustnessIdentifying modeling risk factorsStrategies to managing model robustness
Further reading
Summary
9. Additional Python Machine Learning Tools
Alternative development toolsIntroduction to LasagneGetting to know LasagneIntroduction to TensorFlowGetting to know TensorFlowUsing TensorFlow to iteratively improve our modelsKnowing when to use these libraries
Further reading
Summary
A. Chapter Code Requirements
Index

Content preview from Advanced Machine Learning with Python

Chapter 7. Feature Engineering Part II

Introduction

We have recognized the importance of feature engineering. In the previous chapter, we discussed techniques that enable us to select from a range of features and work effectively to transform our original data into features, which can be effectively processed by the advanced ML algorithms that we have discussed thus far.

The adage garbage in, garbage out is relevant in this context. In earlier chapters, we have seen how image recognition and NLP tasks require carefully-prepared data. In this chapter, we will be looking at a more ubiquitous type of data: quantitative or categorical data that is collected from real-world applications.

Data of the type that we will be working with in this chapter is ...

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Advanced Machine Learning with Python

by John Hearty

Chapter 7. Feature Engineering Part II

Introduction

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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