Book Image

Python Machine Learning By Example - Third Edition

By : Yuxi (Hayden) Liu

Book Image

Python Machine Learning By Example - Third Edition

By: Yuxi (Hayden) Liu

Overview of this book

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.

Preface

Who this book is for

What this book covers

To get the most out of this book

Getting Started with Machine Learning and Python

Getting Started with Machine Learning and Python

An introduction to machine learning

Knowing the prerequisites

Getting started with three types of machine learning

Digging into the core of machine learning

Data preprocessing and feature engineering

Combining models

Installing software and setting up

Free Chapter

Building a Movie Recommendation Engine with Naïve Bayes

Building a Movie Recommendation Engine with Naïve Bayes

Getting started with classification

Exploring Naïve Bayes

Implementing Naïve Bayes

Building a movie recommender with Naïve Bayes

Evaluating classification performance

Tuning models with cross-validation

Recognizing Faces with Support Vector Machine

Recognizing Faces with Support Vector Machine

Finding the separating boundary with SVM

Classifying face images with SVM

Fetal state classification on cardiotocography

Predicting Online Ad Click-Through with Tree-Based Algorithms

Predicting Online Ad Click-Through with Tree-Based Algorithms

A brief overview of ad click-through prediction

Getting started with two types of data – numerical and categorical

Exploring a decision tree from the root to the leaves

Implementing a decision tree from scratch

Implementing a decision tree with scikit-learn

Predicting ad click-through with a decision tree

Ensembling decision trees – random forest

Ensembling decision trees – gradient boosted trees

Predicting Online Ad Click-Through with Logistic Regression

Predicting Online Ad Click-Through with Logistic Regression

Converting categorical features to numerical—one-hot encoding and ordinal encoding

Classifying data with logistic regression

Training a logistic regression model

Training on large datasets with online learning

Handling multiclass classification

Implementing logistic regression using TensorFlow

Feature selection using random forest

Scaling Up Prediction to Terabyte Click Logs

Scaling Up Prediction to Terabyte Click Logs

Learning the essentials of Apache Spark

Programming in PySpark

Learning on massive click logs with Spark

Feature engineering on categorical variables with Spark

Predicting Stock Prices with Regression Algorithms

Predicting Stock Prices with Regression Algorithms

A brief overview of the stock market and stock prices

What is regression?

Mining stock price data

Estimating with linear regression

Estimating with decision tree regression

Estimating with support vector regression

Evaluating regression performance

Predicting stock prices with the three regression algorithms

Predicting Stock Prices with Artificial Neural Networks

Predicting Stock Prices with Artificial Neural Networks

Demystifying neural networks

Building neural networks

Picking the right activation functions

Preventing overfitting in neural networks

Predicting stock prices with neural networks

Mining the 20 Newsgroups Dataset with Text Analysis Techniques

Mining the 20 Newsgroups Dataset with Text Analysis Techniques

How computers understand language – NLP

Touring popular NLP libraries and picking up NLP basics

Getting the newsgroups data

Exploring the newsgroups data

Thinking about features for text data

Visualizing the newsgroups data with t-SNE

Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

Learning without guidance – unsupervised learning

Clustering newsgroups data using k-means

Discovering underlying topics in newsgroups

Machine Learning Best Practices

Machine Learning Best Practices

Machine learning solution workflow

Best practices in the data preparation stage

Best practices in the training sets generation stage

Best practices in the model training, evaluation, and selection stage

Best practices in the deployment and monitoring stage

Categorizing Images of Clothing with Convolutional Neural Networks

Categorizing Images of Clothing with Convolutional Neural Networks

Getting started with CNN building blocks

Architecting a CNN for classification

Exploring the clothing image dataset

Classifying clothing images with CNNs

Boosting the CNN classifier with data augmentation

Improving the clothing image classifier with data augmentation

Making Predictions with Sequences Using Recurrent Neural Networks

Making Predictions with Sequences Using Recurrent Neural Networks

Introducing sequential learning

Learning the RNN architecture by example

Training an RNN model

Overcoming long-term dependencies with Long Short-Term Memory

Analyzing movie review sentiment with RNNs

Writing your own War and Peace with RNNs

Advancing language understanding with the Transformer model

Making Decisions in Complex Environments with Reinforcement Learning

Making Decisions in Complex Environments with Reinforcement Learning

Setting up the working environment

Introducing reinforcement learning with examples

Solving the FrozenLake environment with dynamic programming

Performing Monte Carlo learning

Solving the Taxi problem with the Q-learning algorithm

Other Books You May Enjoy

Other Books You May Enjoy

Index

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Training an RNN model

To explain how we optimize the weights (parameters) of an RNN, we first annotate the weights and the data on the network, as follows:

U denotes the weights connecting the input layer and the hidden layer.
V denotes the weights between the hidden layer and the output layer. Note here that we use only one recurrent layer for simplicity.
W denotes the weights of the recurrent layer; that is, the feedback layer.
x_t denotes the inputs at time step t.
s_t denotes the hidden state at time step t.
h_t denotes the outputs at time step t.

Next, we unfold the simple RNN model over three time steps: t − 1, t, and t + 1, as follows:

Figure 13.9: Unfolding a recurrent layer

We describe the mathematical relationships between the layers as follows:

We let a denote the activation function for the hidden layer. In RNNs, we usually choose tanh or ReLU as the activation function for the hidden layers...