Machine Learning for Decision Sciences with Case Studies in Python
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Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
Not in stock at Prospero.
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Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Edition number 1
- Publisher CRC Press
- Date of Publication 4 October 2024
- ISBN 9781032193571
- Binding Paperback
- No. of pages476 pages
- Size 254x178 mm
- Weight 980 g
- Language English
- Illustrations 259 Illustrations, black & white; 4 Halftones, black & white; 255 Line drawings, black & white; 68 Tables, black & white 592
Categories
Short description:
This book provides a detailed description of machine learning algorithms in Data Analytics, Data Science Lifecycle, Python for Machine Learning, Linear Regression, Logistic Regression and so forth. The focus is on Python programming for machine learning and patterns involved in decision science for handling data including real-world examples.
MoreLong description:
This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data.
Features:
- Explains the basic concepts of Python and its role in machine learning
- Provides comprehensive coverage of feature engineering including real-time case studies
- Perceives the structural patterns with reference to data science and statistics and analytics
- Includes machine learning-based structured exercises
- Appreciates different algorithmic concepts of machine learning including unsupervised, supervised, and reinforcement learning
This book is aimed at researchers, professionals, and graduate students in data science, machine learning, computer science, and electrical and computer engineering.
MoreTable of Contents:
1. Introduction 2. Overview of Python for Machine Learning 3. Data Analytics Life Cycle for Machine Learning 4. Unsupervised Learning 5. Supervised Learning: Regression 6. Supervised Learning: Classification 7. Feature Engineering 8. Reinforcement Learning 9. Case Studies for Decision Sciences Using Python
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