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Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases

Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases

Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases Paperback / softback - 2024

by Yuxi (Hayden) Liu

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Paperback / softback. New. Learn machine learning (ML) with this hands-on guide from best-selling author and ex-Google ML engineer Yuxi (Hayden) Liu. He teaches the basics of ML algorithms to NLP transformers and multimodal models with best practice tips and real-world examples Key Features New and updated content on NLP transformers, PyTorch, and computer vision modeling Best practices have expanded beyond one chapter with tips to improve your ML solutions showcased throughout the book Implement ML algorithms, such as neural networks and decision trees from scratch Book DescriptionThe fourth edition of Python Machine Learning by Example is a comprehensive guide for beginners and experienced ML practitioners who want to learn more advanced techniques like multimodal modeling. Written by best-selling author and ex-Google ML engineer Yuxi (Hayden) Liu, 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-4 and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn advanced 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.What you will learn Follow machine learning best practices for data preparation, model training, and evaluation Build and improve image classifiers using CNNs, transfer learning, and data augmentation Build and fine-tune neural networks using TensorFlow and PyTorch for stock price prediction and image search Analyze sequence data and make predictions using RNNs and transformers Build classifiers using SVMs and boost performance with principal component analysis Learn to avoid overfitting using cross-validation, regularization, feature selection, and dimensionality reduction Who this book is forThis expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python knowledge. The real-world lessons and code prepare anyone undertaking their first serious ML project.
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From the publisher

Author Yuxi (Hayden) Liu teaches machine learning from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-learn, and pandas

Key Features:

- Discover new and updated content on NLP transformers, PyTorch, and computer vision modeling

- Includes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutions

- Implement ML models, such as neural networks and linear and logistic regression, from scratch

- Purchase of the print or Kindle book includes a free PDF copy

Book Description:

The fourth edition of Python Machine Learning by Example is a comprehensive guide for beginners and experienced ML practitioners who want to learn more advanced techniques like multimodal modeling. Written by experienced machine learning author and ex-Google ML engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for ML 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.

What You Will Learn:

- Follow machine learning best practices across data preparation and model development

- Build and improve image classifiers using Convolutional Neural Networks (CNNs) and transfer learning

- Develop and fine-tune neural networks using TensorFlow and PyTorch

- Analyze sequence data and make predictions using RNNs, transformers, and CLIP

- Build classifiers using SVMs and boost performance with PCA

- Avoid overfitting using regularization, feature selection, and more

Who this book is for:

This expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python programming knowledge. The real-world examples, best practices, and code prepare anyone undertaking their first serious ML project.

Table of Contents

- Getting Started with Machine Learning and Python

- Building a Movie Recommendation Engine

- Predicting Online Ad Click-Through with Tree-Based Algorithms

- Predicting Online Ad Click-Through with Logistic Regression

- Predicting Stock Prices with Regression Algorithms

- Predicting Stock Prices with Artificial Neural Networks

- Mining the 20 Newsgroups Dataset with Text Analysis Techniques

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

- Recognizing Faces with Support Vector Machine

- Machine Learning Best Practices

- Categorizing Images of Clothing with Convolutional Neural Networks

- Making Predictions with Sequences Using Recurrent Neural Networks

- Advancing Language Understanding and Generation with Transformer Models

- Building An Image Search Engine Using Multimodal Models

- Making Decisions in Complex Environments with Reinforcement Learning

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