Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases
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Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases
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.
Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases
№ на артикул: 92488569

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

№ на артикул: 92488569

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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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What Stands Out

Practical Learning
Gain hands-on experience with real-world examples, enhancing your understanding of machine learning concepts through practical application and best practices for varied scenarios.
Comprehensive Coverage
Explore a wide range of machine learning techniques and algorithms, ensuring you are equipped with the knowledge to tackle diverse challenges across industries.
Updated Resources
Leverage the latest strategies and approaches in machine learning, with updates reflecting current trends and technologies to ensure relevance in a fast-evolving field.

Информация за продукта

Shop Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases online at a best price in Bulgaria. 1835085628
  • 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 July 31, 2024
Edition 4th
Language English
Print length 518 pages
ISBN-10 1835085628
ISBN-13 978-1835085622
Item Weight 2.12 pounds (960 grams)
Dimensions 7.5 x 1.19 x 9.25 inches (19.1 x 3 x 23.5 cm)

Who Should Buy?

Suitable For
  • Aspiring Data Scientists

    Ideal for beginners looking to build practical machine learning skills through real-world examples and easy-to-follow instructions.

  • Practical Learners

    Great for those who prefer hands-on learning to grasp complex concepts through practical applications and case studies.

  • Professionals Seeking Skills

    Beneficial for working professionals who want to enhance their machine learning knowledge with applicable examples for the industry.

Not Suitable For
  • Total Newcomers

    Not suitable for absolute beginners with no programming knowledge or prior understanding of machine learning concepts.

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Machine Theory Editorial Review

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**** "Python Machine Learning by Example" emerges as a standout resource for both newcomers and those looking to solidify their understanding of machine learning concepts through practical implementations. The fourth edition of this book is particularly welcoming for individuals who might be daunted by complex algorithms, as it excels in simplifying intricate topics with clear explanations paired with real-world use cases. Readers have noted the book's strong emphasis on best practices in machine learning, which is indispensable for aspiring data scientists, machine learning engineers, and analysts. The author not only teaches the "how" behind programming algorithms but delves into the "why," fostering a deeper comprehension of machine learning principles. Moreover, the inclusion of hands-on exercises utilizing popular frameworks such as PyTorch and TensorFlow enables readers to build their competencies through direct application of learned concepts. The book effectively covers a broad range of critical topics, from classification and regression techniques to advanced subjects like deep learning, natural language processing, and reinforcement learning. In doing so, it caters to a wide spectrum of interests and skill levels, ensuring it's relevant for entry-level enthusiasts as well as more experienced practitioners seeking to revisit foundational knowledge. Reviewers particularly appreciated the extensive array of practical examples, such as stock price predictions and image classification with convolutional neural networks (CNNs), which not only engage the reader but also solidify the book's teachings. The structured approach to presenting complex materials makes this book a valuable asset in the toolkit of anyone eager to navigate the evolving landscape of machine learning. Overall, "Python Machine Learning by Example" receives high praise for its accessibility, thorough content, and practical orientation, making it an essential read for those entering or looking to advance in the machine learning field. **

Customer Reviews & Ratings

4.9
77 оценки от клиенти
  • 5 звезда
    93%
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Професионалисти

  • Comprehensive coverage of machine learning concepts with real-world examples
  • Clear explanations that simplify complex algorithms
  • Emphasis on best practices and hands-on exercises
  • Relevant for both beginners and experienced practitioners
  • Incorporates advanced topics like deep learning and NLP
  • Accessible coding exercises using popular frameworks (e.g., TensorFlow, PyTorch)

минуси

  • Some readers find the book outdated for the latest advancements in the field

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€ 38

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Характеристики и предимства

  • 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.

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