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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
SAR 211
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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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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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