Python Machine Leaing By Example: Unlock machine leaing best practices with real-world use cases

by: Yuxi (Hayden) Liu (Author)

Publisher: Packt Publishing

Edition: 4th

Publication Date: 2024/7/31

Language: English

Print Length: 518 pages

ISBN-10: 1835085628

ISBN-13: 9781835085622

Book Description

Author Yuxi (Hayden) Liu teaches machine leaing from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-lea, and pandasKey FeaturesDiscover 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 scratchPurchase of the print or Kindle book includes a free PDF copyBook DescriptionThe fourth edition of Python Machine Leaing By Example is a comprehensive guide for beginners and experienced machine leaing practitioners who want to lea more advanced techniques, such as multimodal modeling. Written by experienced machine leaing author and ex-Google machine leaing engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine leaing 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 lea key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine.This hands-on machine leaing book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine leaing and deep leaing expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine leaing with this authoritative guide.What you will leaFollow machine leaing best practices throughout data preparation and model developmentBuild and improve image classifiers using convolutional neural networks (CNNs) and transfer leaingDevelop 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 moreWho this book is forThis 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 ContentsGetting Started with Machine Leaing and PythonBuilding a Movie Recommendation EnginePredicting Online Ad Click-Through with Tree-Based AlgorithmsPredicting Online Ad Click-Through with Logistic RegressionPredicting Stock Prices with Regression AlgorithmsPredicting Stock Prices with Artificial Neural NetworksMining the 20 Newsgroups Dataset with Text Analysis TechniquesDiscovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic ModelingRecognizing Faces with Support Vector MachineMachine Leaing Best PracticesCategorizing Images of Clothing with Convolutional Neural NetworksMaking Predictions with Sequences Using Recurrent Neural NetworksAdvancing Language Understanding and Generation with Transformer ModelsBuilding An Image Search Engine Using Multimodal ModelsMaking Decisions in Complex Environments with Reinforcement Leaing

About the Author

Author Yuxi (Hayden) Liu teaches machine leaing from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-lea, and pandasKey FeaturesDiscover 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 scratchPurchase of the print or Kindle book includes a free PDF copyBook DescriptionThe fourth edition of Python Machine Leaing By Example is a comprehensive guide for beginners and experienced machine leaing practitioners who want to lea more advanced techniques, such as multimodal modeling. Written by experienced machine leaing author and ex-Google machine leaing engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine leaing 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 lea key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine.This hands-on machine leaing book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine leaing and deep leaing expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine leaing with this authoritative guide.What you will leaFollow machine leaing best practices throughout data preparation and model developmentBuild and improve image classifiers using convolutional neural networks (CNNs) and transfer leaingDevelop 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 moreWho this book is forThis 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 ContentsGetting Started with Machine Leaing and PythonBuilding a Movie Recommendation EnginePredicting Online Ad Click-Through with Tree-Based AlgorithmsPredicting Online Ad Click-Through with Logistic RegressionPredicting Stock Prices with Regression AlgorithmsPredicting Stock Prices with Artificial Neural NetworksMining the 20 Newsgroups Dataset with Text Analysis TechniquesDiscovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic ModelingRecognizing Faces with Support Vector MachineMachine Leaing Best PracticesCategorizing Images of Clothing with Convolutional Neural NetworksMaking Predictions with Sequences Using Recurrent Neural NetworksAdvancing Language Understanding and Generation with Transformer ModelsBuilding An Image Search Engine Using Multimodal ModelsMaking Decisions in Complex Environments with Reinforcement Leaing