Machine Learning

Author Amit Kumar Das , Saikat Dutt , Subramanian Chandramouli

Release Date: 2018/04/01

ISBN: 9789389588132

Topic:

• Data

30
Chapters

0-1
Hours read

0k
Total Words

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Table of Contents

1. Cover
2. About Pearson
3. Title Page
4. Contents
5. Preface
6. Acknowledgements
7. About the Authors
8. Model Syllabus for Machine Learning
9. Lesson plan
10. 1 Introduction to Machine Learning
    1. 1.1 Introduction
    2. 1.2 What is Human Learning?
    3. 1.3 Types of Human Learning
       1. 1.3.1 Learning under expert guidance
       2. 1.3.2 Learning guided by knowledge gained from experts
       3. 1.3.3 Learning by self
    4. 1.4 What is Machine Learning?
       1. 1.4.1 How do machines learn?
       2. 1.4.2 Well-posed learning problem
    5. 1.5 Types of Machine Learning
       1. 1.5.1 Supervised learning
       2. 1.5.2 Unsupervised learning
       3. 1.5.3 Reinforcement learning
       4. 1.5.4 Comparison – supervised, unsupervised, and reinforcement learning
    6. 1.6 Problems Not To Be Solved Using Machine Learning
    7. 1.7 Applications of Machine Learning
       1. 1.7.1 Banking and finance
       2. 1.7.2 Insurance
       3. 1.7.3 Healthcare
    8. 1.8 State-of-The-Art Languages/Tools In Machine Learning
       1. 1.8.1 Python
       2. 1.8.2 R
       3. 1.8.3 Matlab
       4. 1.8.4 SAS
       5. 1.8.5 Other languages/tools
    9. 1.9 Issues in Machine Learning
    10. 1.10 Summary
11. 2 Preparing to Model
    1. 2.1 Introduction
    2. 2.2 Machine Learning Activities
    3. 2.3 Basic Types of Data in Machine Learning
    4. 2.4 Exploring Structure of Data
       1. 2.4.1 Exploring numerical data
       2. 2.4.2 Plotting and exploring numerical data
       3. 2.4.3 Exploring categorical data
       4. 2.4.4 Exploring relationship between variables
    5. 2.5 Data Quality and Remediation
       1. 2.5.1 Data quality
       2. 2.5.2 Data remediation
    6. 2.6 Data Pre-Processing
       1. 2.6.1 Dimensionality reduction
       2. 2.6.2 Feature subset selection
    7. 2.7 Summary
12. 3 Modelling and Evaluation
    1. 3.1 Introduction
    2. 3.2 Selecting a Model
       1. 3.2.1 Predictive models
       2. 3.2.2 Descriptive models
    3. 3.3 Training a Model (for Supervised Learning)
       1. 3.3.1 Holdout method
       2. 3.3.2 K-fold Cross-validation method
       3. 3.3.3 Bootstrap sampling
       4. 3.3.4 Lazy vs. Eager learner
    4. 3.4 Model Representation and Interpretability
       1. 3.4.1 Underfitting
       2. 3.4.2 Overfitting
       3. 3.4.3 Bias – variance trade-off
    5. 3.5 Evaluating Performance of a Model
       1. 3.5.1 Supervised learning – classification
       2. 3.5.2 Supervised learning – regression
       3. 3.5.3 Unsupervised learning – clustering
    6. 3.6 Improving Performance of a Model
    7. 3.7 Summary
13. 4 Basics of Feature Engineering
    1. 4.1 Introduction
       1. 4.1.1 What is a feature?
       2. 4.1.2 What is feature engineering?
    2. 4.2 Feature Transformation
       1. 4.2.1 Feature construction
       2. 4.2.2 Feature extraction
    3. 4.3 Feature Subset Selection
       1. 4.3.1 Issues in high-dimensional data
       2. 4.3.2 Key drivers of feature selection – feature relevance and redundancy
       3. 4.3.3 Measures of feature relevance and redundancy
       4. 4.3.4 Overall feature selection process
       5. 4.3.5 Feature selection approaches
    4. 4.4 Summary
14. 5 Brief Overview of Probability
    1. 5.1 Introduction
    2. 5.2 Importance of Statistical Tools in Machine Learning
    3. 5.3 Concept of Probability – Frequentist and Bayesian Interpretation
       1. 5.3.1 A brief review of probability theory
    4. 5.4 Random Variables
       1. 5.4.1 Discrete random variables
       2. 5.4.2 Continuous random variables
    5. 5.5 Some Common Discrete Distributions
       1. 5.5.1 Bernoulli distributions
       2. 5.5.2 Binomial distribution
       3. 5.5.3 The multinomial and multinoulli distributions
       4. 5.5.4 Poisson distribution
    6. 5.6 Some Common Continuous Distributions
       1. 5.6.1 Uniform distribution
       2. 5.6.2 Gaussian (normal) distribution
       3. 5.6.3 The laplace distribution
    7. 5.7 Multiple Random Variables
       1. 5.7.1 Bivariate random variables
       2. 5.7.2 Joint distribution functions
       3. 5.7.3 Joint probability mass functions
       4. 5.7.4 Joint probability density functions
       5. 5.7.5 Conditional distributions
       6. 5.7.6 Covariance and correlation
    8. 5.8 Central Limit Theorem
    9. 5.9 Sampling Distributions
       1. 5.9.1 Sampling with replacement
       2. 5.9.2 Sampling without replacement
       3. 5.9.3 Mean and variance of sample
    10. 5.10 Hypothesis Testing
    11. 5.11 Monte Carlo Approximation
    12. 5.12 Summary
15. 6 Bayesian Concept Learning
    1. 6.1 Introduction
    2. 6.2 Why Bayesian Methods are Important?
    3. 6.3 Bayes’ Theorem
       1. 6.3.1 Prior
       2. 6.3.2 Posterior
       3. 6.3.3 Likelihood
    4. 6.4 Bayes’ Theorem and Concept Learning
       1. 6.4.1 Brute-force Bayesian algorithm
       2. 6.4.2 Concept of consistent learners
       3. 6.4.3 Bayes optimal classifier
       4. 6.4.4 Naïve Bayes classifier
       5. 6.4.5 Applications of Naïve Bayes classifier
       6. 6.4.6 Handling Continuous Numeric Features in Naïve Bayes Classifier
    5. 6.5 Bayesian Belief Network
       1. 6.5.1 Independence and conditional independence
       2. 6.5.2 Use of the Bayesian Belief network in machine learning
    6. 6.6 Summary
16. 7 Supervised Learning : Classification
    1. 7.1 Introduction
    2. 7.2 Example of Supervised Learning
    3. 7.3 Classification Model
    4. 7.4 Classification Learning Steps
    5. 7.5 Common Classification Algorithms
       1. 7.5.1 k-Nearest Neighbour (kNN)
       2. 7.5.2 Decision tree
       3. 7.5.3 Random forest model
       4. 7.5.4 Support vector machines
    6. 7.6 Summary
17. 8 Super vised Learning : Regression
    1. 8.1 Introduction
    2. 8.2 Example of Regression
    3. 8.3 Common Regression Algorithms
       1. 8.3.1 Simple linear regression
       2. 8.3.2 Multiple linear regression
       3. 8.3.3 Assumptions in Regression Analysis
       4. 8.3.4 Main Problems in Regression Analysis
       5. 8.3.5 Improving Accuracy of the Linear Regression Model
       6. 8.3.6 Polynomial Regression Model
       7. 8.3.7 Logistic Regression
       8. 8.3.8 Maximum Likelihood Estimation
    4. 8.4 Summary
18. 9 Unsupervised Learning
    1. 9.1 Introduction
    2. 9.2 Unsupervised vs Supervised Learning
    3. 9.3 Application of Unsupervised Learning
    4. 9.4 Clustering
       1. 9.4.1 Clustering as a machine learning task
       2. 9.4.2 Different types of clustering techniques
       3. 9.4.3 Partitioning methods
       4. 9.4.4 K-Medoids: a representative object-based technique
       5. 9.4.5 Hierarchical clustering
       6. 9.4.6 Density-based methods - DBSCAN
    5. 9.5 Finding Pattern using Association Rule
       1. 9.5.1 Definition of common terms
       2. 9.5.2 Association rule
       3. 9.5.3 The apriori algorithm for association rule learning
       4. 9.5.4 Build the apriori principle rules
    6. 9.6 Summary
19. 10 Basics of Neural Network
    1. 10.1 Introduction
    2. 10.2 Understanding the Biological Neuron
    3. 10.3 Exploring the Artificial Neuron
    4. 10.4 Types of Activation Functions
       1. 10.4.1 Identity function
       2. 10.4.2 Threshold/step function
       3. 10.4.3 ReLU (Rectified Linear Unit) function
       4. 10.4.4 Sigmoid function
       5. 10.4.5 Hyperbolic tangent function
    5. 10.5 Early Implementations of ANN
       1. 10.5.1 McCulloch–Pitts model of neuron
       2. 10.5.2 Rosenblatt’s perceptron
       3. 10.5.3 ADALINE network model
    6. 10.6 Architectures of Neural Network
       1. 10.6.1 Single-layer feed forward network
       2. 10.6.2 Multi-layer feed forward ANNs
       3. 10.6.3 Competitive network
       4. 10.6.4 Recurrent network
    7. 10.7 Learning Process in ANN
       1. 10.7.1 Number of layers
       2. 10.7.2 Direction of signal flow
       3. 10.7.3 Number of nodes in layers
       4. 10.7.4 Weight of interconnection between neurons
    8. 10.8 Backpropagation
    9. 10.9 Deep Learning
    10. 10.10 Summary
20. 11 Other Types of Learning
    1. 11.1 Introduction
    2. 11.2 Representation Learning
       1. 11.2.1 Supervised neural networks and multilayer perceptron
       2. 11.2.2 Independent component analysis (Unsupervised)
       3. 11.2.3 Autoencoders
       4. 11.2.4 Various forms of clustering
    3. 11.3 Active Learning
       1. 11.3.1 Heuristics for active learning
       2. 11.3.2 Active learning query strategies
    4. 11.4 Instance-Based Learning (Memory-based Learning)
       1. 11.4.1 Radial basis function
       2. 11.4.2 Pros and cons of instance-based learning method
    5. 11.5 Association Rule Learning Algorithm
       1. 11.5.1 Apriori algorithm
       2. 11.5.2 Eclat algorithm
    6. 11.6 Ensemble Learning Algorithm
       1. 11.6.1 Bootstrap aggregation (Bagging)
       2. 11.6.2 Boosting
       3. 11.6.3 Gradient boosting machines (GBM)
    7. 11.7 Regularization Algorithm
    8. 11.8 Summary
21. Appendix A: Programming Machine Learning in R
22. Appendix B: Programming Machine Learning in Python
23. Appendix C: A Case Study on Machine Learning Application: Grouping Similar Service Requests and Classifying a New One
24. Model Question Paper-1
25. Model Question Paper-2
26. Model Question Paper-3
27. Index
28. Copyright