| Description |
1 online resource |
| Contents |
Intro; Preface; Reading the Book; Acknowledgments; Contents; List of Figures; List of Tables; Chapter 1: Machine Learning Definition and Basics; 1.1 Introduction; 1.1.1 Resurgence of ML; 1.1.2 Relation with Artificial Intelligence (AI); 1.1.3 Machine Learning Problems; 1.2 Matrices; 1.2.1 Vector and Tensors; 1.2.2 Matrix Addition (or Subtraction); 1.2.3 Matrix Transpose; 1.2.4 Matrix Multiplication; 1.2.4.1 Multiplying with a Scalar; 1.2.4.2 Multiplying with Another Matrix; 1.2.4.3 Multiplying with a Vector; 1.2.5 Identity Matrix; 1.2.6 Matrix Inversion; 1.2.7 Solving Equations Using Matrices |
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1.3 Numerical Methods1.4 Probability and Statistics; 1.4.1 Sampling the Distribution; 1.4.2 Random Variables; 1.4.3 Expectation; 1.4.4 Conditional Probability and Distribution; 1.4.5 Maximum Likelihood; 1.5 Linear Algebra; 1.6 Differential Calculus; 1.6.1 Functions; 1.6.2 Slope; 1.7 Computer Architecture; 1.8 Next Steps; Chapter 2: Learning Models; 2.1 Supervised Learning; 2.1.1 Classification Problem; 2.1.2 Regression Problem; 2.2 Unsupervised Learning; 2.3 Semi-supervised Learning; 2.4 Reinforcement Learning; Chapter 3: Regressions; 3.1 Introduction; 3.2 The Model; 3.3 Problem Formulation |
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3.4 Linear Regression3.4.1 Normal Method; 3.4.2 Gradient Descent Method; 3.4.2.1 Determine the Slope at Any Given Point; 3.4.2.2 Initial Value; 3.4.2.3 Correction; 3.4.2.4 Learning Rate; 3.4.2.5 Convergence; 3.4.2.6 Alternate Method for Computing Slope; 3.4.2.7 Putting Gradient Descent in Practice; 3.4.3 Normal Equation Method vs Gradient Descent Method; 3.5 Logistic Regression; 3.5.1 Sigmoid Function; 3.5.2 Cost Function; 3.5.3 Gradient Descent; 3.6 Next Steps; 3.7 Key Takeaways; Chapter 4: Improving Further; 4.1 Nonlinear Contribution; 4.2 Feature Scaling |
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4.5.2.1 Basic Approach for SoftMax4.5.2.2 Loss Function; 4.6 Key Takeaways and Next Steps; Chapter 5: Classification; 5.1 Decision Boundary; 5.1.1 Nonlinear Decision Boundary; 5.2 Skewed Class; 5.2.1 Optimizing Precision vs Recall; 5.2.2 Single Metric; 5.3 Naïve Bayes ́Algorithm; 5.4 Support Vector Machines; 5.4.1 Kernel Selection; Chapter 6: Clustering; 6.1 K-Means; 6.1.1 Basic Algorithm; 6.1.2 Distance Calculation; 6.1.3 Algorithm Pseudo Code; 6.1.4 Cost Function; 6.1.5 Choice of Initial Random Centers; 6.1.6 Number of Clusters; 6.2 K-Nearest Neighbor (KNN); 6.2.1 Weight Consideration |
| Summary |
Just like electricity, Machine Learning will revolutionize our life in many ways - some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (EBSCO, viewed May 9, 2019) |
| Subject |
Machine learning.
|
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COMPUTERS -- General.
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Machine learning
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| Form |
Electronic book
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| Author |
Ravi, Ajay, author
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Churiwala, Sanjay, author
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| ISBN |
9783030157296 |
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3030157296 |
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3030157288 |
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9783030157289 |
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9783030157302 |
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303015730X |
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9783030157319 |
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3030157318 |
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