Description
Book SynopsisThis 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
Table 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
