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Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn, 3rd Edition
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Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn, 3rd Edition
Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML) and provides actionable insights on the key fundamentals of ML with Python programming.
Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn, 3rd Edition
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Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn, 3rd Edition

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Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML) and provides actionable insights on the key fundamentals of ML with Python programming.
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Hvad der skiller sig ud

Comprehensive Coverage
Covers major machine learning frameworks like TensorFlow, PyTorch, and Scikit-learn, providing readers with a rich toolkit for building intelligent systems.
Practical Examples
Includes real-world examples that illustrate machine learning concepts, enabling readers to apply knowledge effectively and solve actual problems.
Updated Content
Third edition includes the latest advancements in machine learning, ensuring readers are equipped with current knowledge and techniques for robust system development.

Produktdetaljer

Shop Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn, 3rd Edition online at a best price in Faroe Islands. 1800209711
  • Comprehensive guide to practical machine learning with Python, TensorFlow 2, PyTorch, and scikit-learn
  • Exploration of cutting-edge content on deep learning and reinforcement learning
  • Use of Python libraries such as TensorFlow, PyTorch, and scikit-learn to track machine learning projects
  • New chapters covering topics like movie recommendation, face recognition, stock price prediction, and more
  • Focus on important concepts in ML and data science using Python
  • Target audience includes machine learning enthusiasts, data analysts, and data engineers with prior knowledge of Python coding
Publisher Packt Publishing
Publication date 30 Oct. 2020
Edition 3rd
Language English
Print length 526 pages
ISBN-10 1800209711
ISBN-13 978-1800209718
Item weight 894 g
Dimensions 19.05 x 3.02 x 23.5 cm

Hvem bør købe?

Suitable For
  • Aspiring Data Scientists

    Ideal for beginners seeking hands-on experience with machine learning concepts using popular Python libraries.

  • Software Developers

    Useful for developers wanting to integrate machine learning capabilities into existing applications with practical examples.

  • Machine Learning Enthusiasts

    Great for individuals looking to deepen their understanding of machine learning algorithms through practical projects.

Not Suitable For
  • Complete Beginners

    Not suitable for those with no programming background, as basic Python knowledge is prerequisites for effective learning.

Produktbeskrivelse

Har du spørgsmål? Chat med os

Kundespørgsmål og svar

  • spørgsmål: What additional content is included in 3rd edition of Python Machine Learning By Example?

    svar: The 3rd edition of the book includes six new chapters covering movie recommendation engine development with Naive Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions.
  • spørgsmål: What tools and libraries are used to implement ML algorithms in the book?

    svar: Python Machine Learning By Example, Third Edition uses updated Python libraries such as TensorFlow, PyTorch, and scikit-learn to track machine learning projects end-to-end.
  • spørgsmål: What is the intended audience for the book?

    svar: The book is intended for machine learning enthusiasts, data analysts, or data engineers who are highly passionate about machine learning and want to begin working on machine learning assignments. Prior knowledge of Python programming is assumed, and basic familiarity with statistical concepts will be beneficial.

AI & Machine Learning Editorial Review

Python Machine Learning By Example Build Intelligent Systems Using Python, TensorFlow 2, PyTorch, And Scikit-learn, 3rd Edition is a comprehensive resource for anyone eager to dive into machine learning with practical experience. This edition published by Packt Publishing not only covers algorithms but also emphasizes real-world applications such as facial recognition and stock price prediction. Many readers appreciate the book's clear explanations and step-by-step approach, making complex concepts more accessible. Although some content may seem outdated, readers find it a valuable guide, especially due to its application-oriented style and coverage of essential ML practices that enhance skill sets for modern enterprise needs.

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Fordele

  • Clear explanations of complex concepts
  • Application-oriented with real-world examples
  • Includes sections on ML best practices
  • Thorough coverage of key ML algorithms
  • Valuable resource for ML enthusiasts

Ulemper

  • Some content may feel slightly outdated

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Egenskaber og fordele

  • Comprehensive guide to latest developments of practical machine learning with Python and upgrade understanding of ML algorithms and techniques
  • Explore advanced content reflecting deep and reinforcement learning developments
  • Implement ML algorithms from scratch in Python
  • Gain understanding of mechanics of ML techniques and important concepts in ML and data science
  • Includes realistic examples that provide insight in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP
  • Intended audience - machine learning enthusiast, data analyst, or data engineer passionate about machine learning assignments with prior knowledge of Python programming

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