Liu Python Machine Learning By Example

Unlock machine learning best practices with real-world use cases
1. Auflage 2024
ISBN: 978-1-83508-222-5
Verlag: Packt Publishing
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 526 Seiten

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.

Autoren/Hrsg.

Liu, Yuxi (Hayden)

Leseproben

Preface

The fourth edition of is a comprehensive guide for beginners, and experienced Machine Learning (ML) practitioners who want to learn more advanced techniques like multimodal modeling. This edition emphasizes best practices, providing invaluable insights for ML engineers, data scientists, and analysts.

Explore advanced techniques, including two new chapters on NLP 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 book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your ML expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.

Who this book is for

If you’re a machine learning enthusiast, data analyst, or data engineer who’s highly passionate about machine learning and you want to begin working on ML assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial, although this is not necessary.

What this book covers

, , will kick off your Python machine learning journey. It starts with what machine learning is, why we need it, and its evolution over the last few decades. It then discusses typical machine learning tasks and explores several essential techniques of working with data and working with models, in a practical and fun way. You will also set up the software and tools needed for examples and projects in the upcoming chapters.

, , focuses on classification, specifically binary classification and Naïve Bayes. The goal of the chapter is to build a movie recommendation system. You will learn the fundamental concepts of classification, and about Naïve Bayes, a simple yet powerful algorithm. It also demonstrates how to fine-tune a model, which is an important skill for every data science or machine learning practitioner to learn.

, , introduces and explains in depth tree-based algorithms (including decision trees, random forests, and boosted trees) throughout the course of solving the advertising click-through rate problem. You will explore decision trees from the root to the leaves, and work on implementations of tree models from scratch, using scikit-learn and XGBoost. Feature importance, feature selection, and ensemble will be covered alongside.

, , is a continuation of the ad click-through prediction project, with a focus on a very scalable classification model—logistic regression. You will explore how logistic regression works, and how to work with large datasets. The chapter also covers categorical variable encoding, L1 and L2 regularization, feature selection, online learning, and stochastic gradient descent.

, , focuses on several popular regression algorithms, including linear regression, regression tree and regression forest. It will encourage you to utilize them to tackle a billion (or trillion) dollar problem—stock price prediction. You will practice solving regression problems using scikit-learn and TensorFlow.

, , introduces and explains in depth neural network models. It covers the building blocks of neural networks, and important concepts such as activation functions, feedforward, and backpropagation. You will start by building the simplest neural network and go deeper by adding more layers to it. We will implement neural networks from scratch, use TensorFlow and PyTorch, and train a neural network to predict stock prices.

, , will start the second step of your learning journey—unsupervised learning. It explores a natural language processing problem—exploring newsgroups data. You will gain hands-on experience in working with text data, especially how to convert words and phrases into machine-readable values and how to clean up words with little meaning. You will also visualize text data using a dimension reduction technique called t-SNE. Finally, you will learn how to represent words with embedding vectors.

, , talks about identifying different groups of observations from data in an unsupervised manner. You will cluster the newsgroups data using the K-means algorithm, and detect topics using non-negative matrix factorization and latent Dirichlet allocation. You will be amused by how many interesting themes you are able to mine from the 20 newsgroups dataset!

, , continues the journey of supervised learning and classification. Specifically, it focuses on multiclass classification and support vector machine classifiers. It discusses how the support vector machine algorithm searches for a decision boundary in order to separate data from different classes. You will implement the algorithm with scikit-learn, and apply it to solve various real-life problems including face recognition.

, , aims to fully prove your learning and get you ready for real-world projects. It includes 21 best practices to follow throughout the entire machine learning workflow.

, , is about using Convolutional Neural Networks (CNNs), a very powerful modern machine learning model, to classify images of clothing. It covers the building blocks and architecture of CNNs, and their implementation using PyTorch. After exploring the data of clothing images, you will develop CNN models to categorize the images into ten classes, and utilize data augmentation and transfer learning techniques to boost the classifier.

, , starts by defining sequential learning, and exploring how Recurrent Neural Networks (RNNs) are well suited for it. You will learn about various types of RNNs and their common applications. You will implement RNNs with PyTorch, and apply them to solve three interesting sequential learning problems: sentiment analysis on IMDb movie reviews, stock price forecasting, and text auto-generation.

, , dives into the Transformer neural network, designed for sequential learning. It focuses on crucial parts of the input sequence and captures long-range relationships better than RNNs. You will explore two cutting-edge Transformer models BERT and GPT, and use them for sentiment analysis and text generation, which surpass the performance achieved in the previous chapter.

, , explores a multimodal model, CLIP, that merges visual and textual data. This powerful model can understand connections between images and text. You will dive into its architecture and how it learns, then build an image search engine. Finally, you will cap it all off with a zero-shot image classification project, pushing the boundaries of what this model can do.

, , is about learning from experience, and interacting with the environment. After exploring the fundamentals of reinforcement learning, you will explore the FrozenLake environment with a simple dynamic programming algorithm. You will learn about Monte Carlo learning and use it for value approximation and control. You will also develop temporal difference algorithms and use Q-learning to solve the taxi problem.

To get the most out of this book

A basic foundation of Python knowledge, basic machine learning algorithms, and some basic Python libraries, such as NumPy and pandas, is assumed in order to create smart cognitive actions for your projects.

Download the example code files

The code bundle for the book is hosted on GitHub at https://github.com/packtjaniceg/Python-Machine-Learning-by-Example-Fourth-Edition/. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://packt.link/gbp/9781835085622.

Conventions used

There are a number of text conventions used throughout this book.

: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter (X) handles. Here is an example:...