£30.00
Machine Learning for Decision Sciences with Case Studies in Python
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Book 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.
Table of Contents
1. Introduction
1.1. Introduction to Data Science
1.2. Describing Structural patterns
1.3. Machine Learning and Statistics
1.4. Relation between artificial intelligence, machine learning, neural networks and deep learning
1.5. Data Science lifecycle
1.6. Key Roles of a data scientist
1.7. Real World examples
1.8. Use Cases
2. Overview of Python for Machine Learning
2.1. Introduction
2.2. Python for Machine Learning
2.3. Setting up Python
2.4. Python Basics
2.5. NumPy Basics
2.6. Matplotlib Basics
2.7. Pandas Basics
2.8. Computational Complexity
2.9. Real World Examples
3. Data Analytics Lifecycle for Machine Learning
3.1. Introduction
3.2. Data Analytics Lifecycle
4. Unsupervised Learning
4.1. Introduction
4.2. Unsupervised Learning
4.3. Evaluation Metrics for Clustering
4.4. Clustering Algorithms
4.5. K-Means Clustering
4.6. Hierarchical Clustering
4.7. Mixture of Gaussians Clustering
4.8. Density Based Clustering
5. Supervised Learning: Regression
5.1. Introduction
5.2. Supervised Learning – Real Life Scenario
5.3. Types of Supervised Learning
5.4. Linear Regression
6. Supervised Learning: Classification
6.1. Introduction
6.2. Use Cases of Classification
6.3. Logistic Regression
6.4. Decision Tree Classifier
6.5. Random Forest Classifier
6.6. Support Vector Machines
7. Feature Engineering
7.1. Introduction
7.2. Feature Selection
7.3. Factor Analysis
7.4. Principal Component Analysis
7.5. Eigen Values and PCA
7.6. Feature Reduction
7.7. PCA Transformation in Practice using Python
7.8. Linear Discriminant Analysis
7.9. LDA Transformation in Practice using Python
8. Reinforcement Learning
8.1. Introduction
8.2. Reinforcement Learning
8.3. How RL differs from other ML algorithms?
8.4. Elements of RL
8.5. Markov Decision Process
8.6. Dynamic Programming
9. Case Studies for Decision Sciences using Python
9.1. Retail Price Optimization using Price Elasticity of Demand Method
9.2. Market Basket Analysis
9.3. Predicting cost of insurance claims for a Property and Causality (P&C) insurance Company
9.4. Ecommerce Product Ranking and Sentiment Analysis
9.5. Sales Prediction of a Retailer
Appendix
Bibliography
Author(s)
Biography
Dr. S. Sumathi is working as a Professor in the Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore with teaching and research experience of 30 years. Her research interests include Neural Networks, Fuzzy Systems and Genetic Algorithms, Pattern Recognition and Classification, Data Warehousing and Data Mining, Operating systems and Parallel Computing. She is the author of more than 40 papers in refereed journals and international conferences. She has authored books with reputed publishers such as Springer and CRC Press.
Dr. L. Ashok Kumar was a Postdoctoral Research Fellow from San Diego State University, California. He is a recipient of the BHAVAN fellowship from the Indo-US Science and Technology Forum and SYST Fellowship from DST, Govt. of India. His current research focuses on integration of Renewable Energy Systems in the Smart Grid and Wearable Electronics. He has 3 years of industrial experience and 19 years of academic and research experience. He has published 167 technical papers in International and National journals and presented 157 papers in National and International Conferences. He has authored 10 books with leading publishers like CRC, Springer and Elsevier. He has completed 26 Government of India funded projects, and currently 7 projects are in progress.
Dr. Suresh Rajappa PhD PMP MBA is seasoned senior IT management consulting professional with 25 years’ experience leading large global IT programs and projects in IT Strategy, Finance IT (FINTECH) Transformation Strategy, BI and data warehousing / Data Analytics and Management for multiple fortune 100 clients across diverse industries, generating millions of dollars to top and bottom lines. Successful recruiting and leading onshore/offshore cross-cultural teams to deliver complex enterprise-wide solutions within tight deadlines and budgets. Highly effective at breaking down strategic program/project initiatives into tactical plans and processes to achieve aggressive customer goals. Excel at leveraging strategic partnerships, global resources, process improvements, and best practices to maximize project delivery performance and ROI. Inspirational, solution-focused leader with exceptional ability managing multimillion-dollar P&Ls/budgets and change management initiatives. As an adjunct professor, Dr. Suresh Rajappa teaches data science for graduate and doctoral students at PSG College of technology. His Industry specializations include Utility, Finance (Banking and Insurance) and HiTech Manufacturing. He is a frequent speaker Microsoft PASS conferences, SAP Financials and SAP TechEd conferences on Data Analytics related topics. He also teaches Data Analytics and IT Project Management for Undergraduate and Graduate level students. He is also key note speaker in International Conference on Artificial Intelligence, Smart Grid and Smart City Applications.
Dr. Surekha Paneerselvam is an Assistant Professor (Sr. Gr) in the Department of Electrical and Electronics Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India with 20 years of experience in teaching, industry and research. She has published 35 papers in International and National journals and conferences. She has authored 7 books with leading publishers such as CRC Press and Springer. Her research interests include Control Systems, Computational Intelligence, Machine Learning, Signal and Image Processing, Embedded Systems, Real time operating systems, and Virtual Instrumentation.