Python Machine Learning By Example Unlock machine learning best practices with real - world use cases, 4th Edition

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Free Download Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases, 4th Edition by Yuxi Liu
English | July 31, 2024 | ISBN: 1835085628 | True PDF | 518 pages | 31.9 MB
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 modelingIncludes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutionsImplement ML models, such as neural networks and linear and logistic regression, from scratch
Book Description
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.
What you will learn
Follow machine learning best practices throughout data preparation and model developmentBuild and improve image classifiers using convolutional neural networks (CNNs) and transfer learningDevelop and fine-tune neural networks using TensorFlow and PyTorchAnalyze sequence data and make predictions using recurrent neural networks (RNNs), transformers, and CLIPBuild classifiers using support vector machines (SVMs) and boost performance with PCAAvoid 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.

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Python Machine Learning by Example With Real-World Use Cases 4ed 2024 - Liu Y.

Language: English | Format: epub | 2024 | Size: 44.6 MB

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 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.

What you will learn
Follow machine learning best practices throughout 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 recurrent neural networks (RNNs), transformers, and CLIP
Build classifiers using support vector machines (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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