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| Doshi, Dr Ruchi, Hiran, Dr Kamal Kant, Jain, Ritesh Kumar, Lakhwani, Dr Kamlesh | ||||
| BPB Publications | ||||
| 2021 | ||||
| English | ||||
| 294 pages | ||||
| 6.34 MB | ||||
[tab] [content title="Summary"] ### Concepts of Machine Learning with Practical Approaches **Key Features** - Real-world examples illustrate how Machine Learning algorithms function. - Graphical and statistical representations simplify modeling for Machine Learning and Neural Networks. - Packed with Python code, numerous exercises, and model question papers for data science students. **Description** This book provides readers with fundamental concepts of Machine Learning in an accessible language. It aims to impart in-depth knowledge of various Machine Learning (ML) algorithms and their practical applications. The book covers a range of Supervised Machine Learning algorithms, including Linear Regression, Naïve Bayes, Decision Trees, K-nearest neighbors, Logistic Regression, Support Vector Machines, and Random Forests. It also explores Unsupervised Machine Learning techniques such as k-means clustering, Hierarchical Clustering, Probabilistic Clustering, Association Rule Mining, Apriori Algorithm, f-p growth algorithm, and Gaussian Mixture Models. Additionally, it discusses Reinforcement Learning algorithms like Markov Decision Processes (MDP), Bellman equations, policy evaluation using Monte Carlo, Policy Iteration, Value Iteration, Q-Learning, and State-Action-Reward-State-Action (SARSA). The book further covers various feature extraction and selection techniques, Recommender Systems, and provides an overview of Deep Learning. **What You Will Learn** - Techniques for feature extraction and selection. - How to choose the best Machine Learning algorithm for specific problems. - Proficiency in popular Python libraries such as Scikit-learn, pandas, and matplotlib. - Practical implementation of various Machine Learning methods. **Who This Book Is For** This book is aimed at data science and analytics students, academics, and researchers interested in exploring Machine Learning concepts and applying them to real-world scenarios. Familiarity with basic statistical and programming concepts is beneficial but not required. **Table of Contents** 1. Introduction 2. Supervised Learning Algorithms 3. Unsupervised Learning 4. Introduction to Statistical Learning Theory 5. Semi-Supervised Learning and Reinforcement Learning 6. Recommender Systems **About the Authors** **Dr. Ruchi Doshi** has over 14 years of experience in academia, research, and software development across Asia and Africa. She is currently a research supervisor at Azteca University, Mexico, and an adjunct faculty member at Jyoti Vidyapeeth Women’s University, Jaipur, India. **Kamal Kant Hiran** serves as an Assistant Professor in the School of Engineering at Sir Padampat Singhania University (SPSU), Udaipur, India, and as a Research Fellow at Aalborg University, Copenhagen, Denmark. He is a Gold Medalist in M.Tech. (Hons.) with over 16 years of academic and research experience in Asia, Africa, and Europe. **Ritesh Kumar Jain** is an Assistant Professor at Geetanjali Institute of Technical Studies (GITS), Udaipur, India, with over 15 years of teaching and research experience. **Dr. Kamlesh Lakhwani** works as an Associate Professor in Computer Science & Engineering at JECRC University, Jaipur, India, bringing 15 years of academic and research experience in Asia. [/content] [content title="Content"] [/content] [content title="Author(s)"] [/content] [/tab]
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