Numéro d'article:
118405976
Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases Paperback – 31 July 2024
Numéro d'article:
118405976
BIF 116940
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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.
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Ce qui se démarque
Real-World Use Cases
Provides practical examples to illustrate machine learning concepts, enhancing understanding and retention through applicable scenarios that reflect real industry challenges.
Best Practices
Equips readers with proven techniques and strategies to effectively implement machine learning projects, ensuring optimal results and minimizing common pitfalls associated with data science endeavors.
Comprehensive Guide
Covers a wide range of machine learning topics, making it suitable for both beginners and experienced practitioners, thus fostering a deeper insight into various machine learning applications.
Détails du produit
- 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.Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader FreeKey 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 scratchBook DescriptionThe 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 learnFollow 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 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 Learning 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 Learning 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 Learning
| Publisher | Packt Publishing |
| Publication date | 31 July 2024 |
| Edition | 4. |
| Language | English |
| Print length | 518 pages |
| ISBN-10 | 1835085628 |
| ISBN-13 | 978-1835085622 |
| Dimensions | 19.05 x 3.02 x 23.5 cm |
À qui est-ce destiné ?
-
Aspiring Data Scientists
Ideal for newcomers wanting practical insights into machine learning through hands-on examples and real-world applications.
-
Developers Transitioning
Perfect for software developers looking to enhance their skills by incorporating machine learning into existing projects.
-
Tech Enthusiasts
Great for enthusiasts eager to understand machine learning strategies along with practical implementation scenarios.
-
Beginners in Coding
Not suitable for complete beginners who lack basic programming knowledge and fundamentals of Python coding.
-
Advanced Practitioners
Less beneficial for experienced machine learning experts seeking advanced theories or cutting-edge research methodologies.
-
Non-technical Users
Not recommended for individuals without a technical background who may struggle with programming concepts and applications.
DESCRIPTION DU PRODUIT
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Questions et réponses des clients
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question:
Is this book suitable for beginners?
répondre: Yes, it's designed for both beginners and experienced practitioners. -
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What programming knowledge do I need?
répondre: Basic Python programming knowledge is required. -
question:
Do I need additional software to follow along?
répondre: You will need access to libraries such as PyTorch and TensorFlow for practical examples.
English edition Yuxi (Hayden) Liu Editorial Review
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BIF 116940
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Caractéristiques et avantages
- Comprehensive guide for all levels in machine learning.
- Emphasizes machine learning best practices throughout.
- Includes hands-on examples using PyTorch, TensorFlow, and scikit-learn.
- Covers advanced techniques like NLP transformers and multimodal models.
- Provides insights from an experienced Google ML engineer.
- Free PDF copy included with print or Kindle purchase.