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Author(s): | Doshi, Dr Ruchi, Hiran, Dr Kamal Kant, Jain, Ritesh Kumar, Lakhwani, Dr Kamlesh
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Publisher: | BPB Publications
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Year: | 2021
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English |
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294 pages |
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Size: | 6.34 MB
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Extension: | PDF |
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[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]
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Key-Words: Télécharger EBOOK PDF EPUB DJVU Machine Learning: Master Supervised and Unsupervised Learning Algorithms with Real Examples (English Edition). Download EBOOK PDF EPUB DJVU Machine Learning: Master Supervised and Unsupervised Learning Algorithms with Real Examples (English Edition).
