Book Image

Building Machine Learning Systems with Python - Third Edition

By : Luis Pedro Coelho, Willi Richert, Matthieu Brucher
Book Image

Building Machine Learning Systems with Python - Third Edition

By: Luis Pedro Coelho, Willi Richert, Matthieu Brucher

Overview of this book

Machine learning enables systems to make predictions based on historical data. Python is one of the most popular languages used to develop machine learning applications, thanks to its extensive library support. This updated third edition of Building Machine Learning Systems with Python helps you get up to speed with the latest trends in artificial intelligence (AI). With this guide’s hands-on approach, you’ll learn to build state-of-the-art machine learning models from scratch. Complete with ready-to-implement code and real-world examples, the book starts by introducing the Python ecosystem for machine learning. You’ll then learn best practices for preparing data for analysis and later gain insights into implementing supervised and unsupervised machine learning techniques such as classification, regression and clustering. As you progress, you’ll understand how to use Python’s scikit-learn and TensorFlow libraries to build production-ready and end-to-end machine learning system models, and then fine-tune them for high performance. By the end of this book, you’ll have the skills you need to confidently train and deploy enterprise-grade machine learning models in Python.

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Table of Contents (17 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Getting Started with Python Machine Learning
Getting Started with Python Machine Learning
Machine learning and Python – a dream team
Summary
2
Classifying with Real-World Examples
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
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
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
Dimensionality Reduction
Sketching our roadmap
Selecting features
Feature projection
Multidimensional scaling
Autoencoders, or neural networks for dimensionality reduction
Summary
6
Clustering – Finding Related Posts
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
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
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
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
Topic Modeling
Latent Dirichlet allocation
Summary
11
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
Music classification using Tensorflow
Summary
12
Computer Vision
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
Reinforcement Learning
Types of reinforcement learning
Excelling at games
Summary
14
Bigger Data
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
15
Where to Learn More About Machine Learning
Where to Learn More About Machine Learning
Online courses
Books
All that was left out
Summary
16
Other Books You May Enjoy
Other Books You May Enjoy
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Using Lasso or ElasticNet in scikit-learn

Let's adapt the preceding example to use ElasticNets. Using scikit-learn, it is very easy to swap in the ElasticNet regressor for the least squares one that we had before:

from sklearn.linear_model import Lasso 
las = Lasso(alpha=0.5)

Now we use las, whereas earlier we used lr. This is the only change that is needed. The results are exactly what we would expect. When using Lasso, the R2 on the training data decreases to 0.71 (it was 0.74 before), but the cross-validation fit is now 0.59 (as opposed to 0.56 with linear regression). We trade a larger error on the training data in order to gain better generalization.

Visualizing the Lasso path

Using scikit-learn, we can easily visualize...

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