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Item #:
121146719
Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn, 3rd Edition
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Item #:
121146719
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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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Product Details
- 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 |
Product Description
Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn, 3rd Edition
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Customer Questions & Answers
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Question:
What additional content is included in 3rd edition of Python Machine Learning By Example?
Answer: 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. -
Question:
What tools and libraries are used to implement ML algorithms in the book?
Answer: 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. -
Question:
What is the intended audience for the book?
Answer: 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.
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Features & Benefits
- 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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