Building Machine Learning Systems with Python - Third Edition

Willi Richert, Luis Pedro Coelho, Et al

July 2018

406 pages

12 hours 10 minutes

Get more from your data by creating practical machine learning systems with Python

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Learn	Build a classification system that can be applied to text, images, and sound Employ Amazon Web Services (AWS) to run analysis on the cloud Solve problems related to regression using scikit-learn and TensorFlow Recommend products to users based on their past purchases Understand different ways to apply deep neural networks on structured data Address recent developments in the field of computer vision and reinforcement learning
About	Machine learning allows systems to learn things without being explicitly programmed to do so. Python is one of the most popular languages used to develop machine learning applications, which take advantage of its extensive library support. This third edition of Building Machine Learning Systems with Python addresses recent developments in the field by covering the most-used datasets and libraries to help you build practical machine learning systems. Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python, being a dynamic language, allows for fast exploration and experimentation. This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and being introduced to libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling and creating recommendation systems. With Building Machine Learning Systems with Python, you’ll gain the tools and understanding required to build your own systems, all tailored to solve real-world data analysis problems. By the end of this book, you will be able to build machine learning systems using techniques and methodologies such as classification, sentiment analysis, computer vision, reinforcement learning, and neural networks.
Features	Develop your own Python-based machine learning system Discover how Python offers multiple algorithms for modern machine learning systems Explore key Python machine learning libraries to implement in your projects
Page Count	406
Course Length	12 hours 10 minutes
ISBN	9781788623223
Date Of Publication	30 Jul 2018

Table of contents

1 Getting Started with Python Machine Learning

Machine learning and Python – a dream team
Summary

2 Classifying with Real-World Examples

The Iris dataset
Evaluation – holding out data and cross-validation
How to measure and compare classifiers
A more complex dataset and the nearest-neighbor classifier
Which classifier to use
Summary

3 Regression

Predicting house prices with regression
Multidimensional regression
Cross-validation for regression
Using Lasso or ElasticNet in scikit-learn
Regression with TensorFlow
Summary

4 Classification I – Detecting Poor Answers

Sketching our roadmap
Learning to classify classy answers
Fetching the data
Creating our first classifier
Deciding how to improve the performance
Using logistic regression
Looking behind accuracy – precision and recall
Slimming the classifier
Ship it!
Classification using Tensorflow
Summary

5 Dimensionality Reduction

Sketching our roadmap
Selecting features
Feature projection
Multidimensional scaling
Autoencoders, or neural networks for dimensionality reduction
Summary

6 Clustering – Finding Related Posts

Measuring the relatedness of posts
Preprocessing – similarity measured as a similar number of common words
Clustering
Solving our initial challenge
Tweaking the parameters
Summary

7 Recommendations

Rating predictions and recommendations
Splitting into training and testing
Normalizing the training data
A neighborhood approach to recommendations
A regression approach to recommendations
Combining multiple methods
Basket analysis
Association rule mining
Summary

8 Artificial Neural Networks and Deep Learning

Using TensorFlow
Saving and restoring neural networks
LSTM for predicting text
LSTM for image processing
Summary

9 Classification II – Sentiment Analysis

Sketching our roadmap
Fetching the Twitter data
Introducing the Naïve Bayes classifier
Creating our first classifier and tuning it
Cleaning tweets
Taking the word types into account
Summary

10 Topic Modeling

Latent Dirichlet allocation
Summary

11 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
Music classification using Tensorflow
Summary

12 Computer Vision

Introducing image processing
Basic image classification
Computing features from images
Writing your own features
Using features to find similar images
Classifying a harder dataset
Local feature representations
Image generation with adversarial networks
Summary

13 Reinforcement Learning

Types of reinforcement learning
Excelling at games
Summary

14 Bigger Data

Learning about big data
Looking under the hood
Using jug for data analysis
Reusing partial results
Using Amazon Web Services
Creating your first virtual machines
Installing Python packages on Amazon Linux
Running jug on our cloud machine
Automating the generation of clusters with cfncluster
Summary

Authors

Willi Richert

Willi Richert has a PhD in machine learning/robotics, where he has used reinforcement learning, hidden Markov models, and Bayesian networks to let heterogeneous robots learn by imitation. Now at Microsoft, he is involved in various machine learning areas, such as deep learning, active learning, or statistical machine translation. Willi started as a child with BASIC on his Commodore 128. Later, he discovered Turbo Pascal, then Java, then C++ - only to finally arrive at his true love: Python.

Luis Pedro Coelho

Luis Pedro Coelho is a computational biologist who analyzes DNA from microbial communities to characterize their behavior. He has also worked extensively in bioimage informatics - the application of machine learning techniques for the analysis of images of biological specimens. His main focus is on the processing and integration of large-scale datasets. He has a PhD from Carnegie Mellon University and has authored several scientific publications. In 2004, he began developing in Python and has contributed to several open source libraries. He is currently a faculty member at Fudan University in Shanghai.

Matthieu Brucher

Matthieu Brucher is a computer scientist who specializes in high-performance computing and computational modeling and currently works for JPMorgan in their quantitative research branch. He is also the lead developer of Audio ToolKit, a library for real-time audio signal processing. He has a PhD in machine learning and signals processing from the University of Strasbourg, two Master of Science degrees—one in digital electronics and signal processing and another in automation – from the University of Paris XI and Supelec, as well as a Master of Music degree from Bath Spa University.