- Cover
- About Pearson
- Title Page
- Contents
- Preface
- Acknowledgements
- About the Authors
- Model Syllabus for Machine Learning
- Lesson plan
- 1 Introduction to Machine Learning
- 1.1 Introduction
- 1.2 What is Human Learning?
- 1.3 Types of Human Learning
- 1.4 What is Machine Learning?
- 1.5 Types of Machine Learning
- 1.6 Problems Not To Be Solved Using Machine Learning
- 1.7 Applications of Machine Learning
- 1.8 State-of-The-Art Languages/Tools In Machine Learning
- 1.9 Issues in Machine Learning
- 1.10 Summary
- 2 Preparing to Model
- 3 Modelling and Evaluation
- 4 Basics of Feature Engineering
- 5 Brief Overview of Probability
- 5.1 Introduction
- 5.2 Importance of Statistical Tools in Machine Learning
- 5.3 Concept of Probability – Frequentist and Bayesian Interpretation
- 5.4 Random Variables
- 5.5 Some Common Discrete Distributions
- 5.6 Some Common Continuous Distributions
- 5.7 Multiple Random Variables
- 5.8 Central Limit Theorem
- 5.9 Sampling Distributions
- 5.10 Hypothesis Testing
- 5.11 Monte Carlo Approximation
- 5.12 Summary
- 6 Bayesian Concept Learning
- 7 Supervised Learning : Classification
- 8 Super vised Learning : Regression
- 9 Unsupervised Learning
- 10 Basics of Neural Network
- 11 Other Types of Learning
- Appendix A: Programming Machine Learning in R
- Appendix B: Programming Machine Learning in Python
- Appendix C: A Case Study on Machine Learning Application: Grouping Similar Service Requests and Classifying a New One
- Model Question Paper-1
- Model Question Paper-2
- Model Question Paper-3
- Index
- Copyright
Lesson Plan
-
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