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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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產品詳情
- 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 |
產品敘述
Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn, 3rd Edition
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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 Although some content may seem outdated, readers find it a valuable guide, especially due to its application-oriented style and coverage of essential ML practices that enhance skill sets for modern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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優點
- 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
缺點
- Some content may feel slightly outdated
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特色和優勢
- 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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