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Python Machine Learning By Example

By : Yuxi (Hayden) Liu

4.9 (9)

Python Machine Learning By Example

4.9 (9)

By: Yuxi (Hayden) Liu

Overview of this book

The fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts. Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine. This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.

Preface

Preface

Who this book is for

What this book covers

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Free Chapter

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

Summary

Exercises

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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

Summary

Exercises

References

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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 forests

Ensembling decision trees – gradient-boosted trees

Summary

Exercises

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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

Summary

Exercises

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Predicting Stock Prices with Regression Algorithms

Predicting Stock Prices with Regression Algorithms

What is regression?

Mining stock price data

Getting started with feature engineering

Estimating with linear regression

Estimating with decision tree regression

Implementing a regression forest

Evaluating regression performance

Predicting stock prices with the three regression algorithms

Summary

Exercises

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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

Summary

Exercises

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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

Summary

Exercises

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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

Getting started with k-means clustering

Clustering newsgroups dataset

Discovering underlying topics in newsgroups

Summary

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Recognizing Faces with Support Vector Machine

Recognizing Faces with Support Vector Machine

Finding the separating boundary with SVM

Classifying face images with SVM

Estimating with support vector regression

Summary

Exercises

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Machine Learning Best Practices

Machine Learning Best Practices

Machine learning solution workflow

Best practices in the data preparation stage

Best practices in the training set generation stage

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

Best practices in the deployment and monitoring stage

Summary

Exercises

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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

Advancing the CNN classifier with transfer learning

Summary

Exercises

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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 LSTM

Analyzing movie review sentiment with RNNs

Revisiting stock price forecasting with LSTM

Writing your own War and Peace with RNNs

Summary

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Advancing Language Understanding and Generation with the Transformer Models

Advancing Language Understanding and Generation with the Transformer Models

Understanding self-attention

Exploring the Transformer’s architecture

Improving sentiment analysis with BERT and Transformers

Generating text using GPT

Summary

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Building an Image Search Engine Using CLIP: a Multimodal Approach

Building an Image Search Engine Using CLIP: a Multimodal Approach

Introducing the CLIP model

Getting started with the dataset

Finding images with words

Summary

Exercises

References

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Making Decisions in Complex Environments with Reinforcement Learning

Making Decisions in Complex Environments with Reinforcement Learning

Setting up the working environment

Introducing OpenAI Gym and Gymnasium

Introducing reinforcement learning with examples

Solving the FrozenLake environment with dynamic programming

Performing Monte Carlo learning

Solving the Blackjack problem with the Q-learning algorithm

Summary

Exercises

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Other Books You May Enjoy

Other Books You May Enjoy

Index

Index

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What is regression?

Regression is one of the main types of supervised learning in machine learning. In regression, the training set contains observations (also called features) and their associated continuous target values. The process of regression has two phases:

The first phase is exploring the relationships between the observations and the targets. This is the training phase.
The second phase is using the patterns from the first phase to generate the target for a future observation. This is the prediction phase.

The overall process is depicted in the following diagram:

Figure 5.1: Training and prediction phase in regression

The major difference between regression and classification is that the output values in regression are continuous, while in classification they are discrete. This leads to different application areas for these two supervised learning methods. Classification is basically used to determine desired memberships or characteristics...

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