Machine learning for decision sciences with case studies in Python

"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. 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 industry. 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, Electrical, and Computer Engineering"--

<P>1. Introduction <BR>1.1. Introduction to Data Science <BR>1.2. Describing Structural patterns <BR>1.3. Machine Learning and Statistics<BR>1.4. Relation between artificial intelligence, machine learning, neural networks and deep learning<BR>1.5. Data Science lifecycle<BR>1.6. Key Roles of a data scientist<BR>1.7. Real World examples<BR>1.8. Use Cases</P><P>2. Overview of Python for Machine Learning <BR>2.1. Introduction <BR>2.2. Python for Machine Learning<BR>2.3. Setting up Python<BR>2.4. Python Basics<BR>2.5. NumPy Basics<BR>2.6. Matplotlib Basics<BR>2.7. Pandas Basics<BR>2.8. Computational Complexity<BR>2.9. Real World Examples</P><P>3. Data Analytics Lifecycle for Machine Learning <BR>3.1. Introduction<BR>3.2. Data Analytics Lifecycle </P><P>4. Unsupervised Learning<BR>4.1. Introduction<BR>4.2. Unsupervised Learning<BR>4.3. Evaluation Metrics for Clustering<BR>4.4. Clustering Algorithms<BR>4.5. K-Means Clustering<BR>4.6. Hierarchical Clustering<BR>4.7. Mixture of Gaussians Clustering <BR>4.8. Density Based Clustering</P><P>5. Supervised Learning: Regression<BR>5.1. Introduction<BR>5.2. Supervised Learning -- Real Life Scenario<BR>5.3. Types of Supervised Learning<BR>5.4. Lin…