book

Machine Learning

Name: Machine Learning
ISBN: 9789389588132

by Subramanian Chandramouli, Saikat Dutt, Amit Kumar Das

April 2018

Intermediate to advanced

456 pages

11h 47m

English

Pearson Education India

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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 Introduction1.2 What is Human Learning?1.3 Types of Human Learning1.3.1 Learning under expert guidance 1.3.2 Learning guided by knowledge gained from experts1.3.3 Learning by self1.4 What is Machine Learning?1.4.1 How do machines learn?1.4.2 Well-posed learning problem1.5 Types of Machine Learning1.5.1 Supervised learning1.5.2 Unsupervised learning1.5.3 Reinforcement learning1.5.4 Comparison – supervised, unsupervised, and reinforcement learning1.6 Problems Not To Be Solved Using Machine Learning1.7 Applications of Machine Learning1.7.1 Banking and finance1.7.2 Insurance1.7.3 Healthcare1.8 State-of-The-Art Languages/Tools In Machine Learning1.8.1 Python1.8.2 R1.8.3 Matlab1.8.4 SAS1.8.5 Other languages/tools1.9 Issues in Machine Learning1.10 Summary

2 Preparing to Model
2.1 Introduction2.2 Machine Learning Activities2.3 Basic Types of Data in Machine Learning2.4 Exploring Structure of Data2.4.1 Exploring numerical data2.4.2 Plotting and exploring numerical data2.4.3 Exploring categorical data2.4.4 Exploring relationship between variables2.5 Data Quality and Remediation2.5.1 Data quality2.5.2 Data remediation2.6 Data Pre-Processing2.6.1 Dimensionality reduction2.6.2 Feature subset selection2.7 Summary
3 Modelling and Evaluation
3.1 Introduction3.2 Selecting a Model3.2.1 Predictive models3.2.2 Descriptive models3.3 Training a Model (for Supervised Learning)3.3.1 Holdout method3.3.2 K-fold Cross-validation method3.3.3 Bootstrap sampling3.3.4 Lazy vs. Eager learner3.4 Model Representation and Interpretability3.4.1 Underfitting3.4.2 Overfitting3.4.3 Bias – variance trade-off3.5 Evaluating Performance of a Model3.5.1 Supervised learning – classification3.5.2 Supervised learning – regression3.5.3 Unsupervised learning – clustering3.6 Improving Performance of a Model3.7 Summary
4 Basics of Feature Engineering
4.1 Introduction4.1.1 What is a feature?4.1.2 What is feature engineering?4.2 Feature Transformation4.2.1 Feature construction4.2.2 Feature extraction4.3 Feature Subset Selection4.3.1 Issues in high-dimensional data4.3.2 Key drivers of feature selection – feature relevance and redundancy4.3.3 Measures of feature relevance and redundancy4.3.4 Overall feature selection process4.3.5 Feature selection approaches4.4 Summary
5 Brief Overview of Probability
5.1 Introduction5.2 Importance of Statistical Tools in Machine Learning5.3 Concept of Probability – Frequentist and Bayesian Interpretation5.3.1 A brief review of probability theory5.4 Random Variables5.4.1 Discrete random variables5.4.2 Continuous random variables5.5 Some Common Discrete Distributions5.5.1 Bernoulli distributions5.5.2 Binomial distribution5.5.3 The multinomial and multinoulli distributions5.5.4 Poisson distribution5.6 Some Common Continuous Distributions5.6.1 Uniform distribution5.6.2 Gaussian (normal) distribution5.6.3 The laplace distribution5.7 Multiple Random Variables5.7.1 Bivariate random variables5.7.2 Joint distribution functions5.7.3 Joint probability mass functions5.7.4 Joint probability density functions5.7.5 Conditional distributions 5.7.6 Covariance and correlation 5.8 Central Limit Theorem5.9 Sampling Distributions5.9.1 Sampling with replacement5.9.2 Sampling without replacement5.9.3 Mean and variance of sample5.10 Hypothesis Testing5.11 Monte Carlo Approximation5.12 Summary
6 Bayesian Concept Learning
6.1 Introduction6.2 Why Bayesian Methods are Important?6.3 Bayes’ Theorem6.3.1 Prior6.3.2 Posterior6.3.3 Likelihood6.4 Bayes’ Theorem and Concept Learning6.4.1 Brute-force Bayesian algorithm6.4.2 Concept of consistent learners6.4.3 Bayes optimal classifier6.4.4 Naïve Bayes classifier6.4.5 Applications of Naïve Bayes classifier6.4.6 Handling Continuous Numeric Features in Naïve Bayes Classifier6.5 Bayesian Belief Network6.5.1 Independence and conditional independence6.5.2 Use of the Bayesian Belief network in machine learning6.6 Summary
7 Supervised Learning : Classification
7.1 Introduction7.2 Example of Supervised Learning7.3 Classification Model7.4 Classification Learning Steps7.5 Common Classification Algorithms7.5.1 k-Nearest Neighbour (kNN)7.5.2 Decision tree7.5.3 Random forest model7.5.4 Support vector machines7.6 Summary
8 Super vised Learning : Regression
8.1 Introduction8.2 Example of Regression8.3 Common Regression Algorithms8.3.1 Simple linear regression8.3.2 Multiple linear regression8.3.3 Assumptions in Regression Analysis8.3.4 Main Problems in Regression Analysis8.3.5 Improving Accuracy of the Linear Regression Model8.3.6 Polynomial Regression Model8.3.7 Logistic Regression8.3.8 Maximum Likelihood Estimation 8.4 Summary
9 Unsupervised Learning
9.1 Introduction9.2 Unsupervised vs Supervised Learning9.3 Application of Unsupervised Learning9.4 Clustering9.4.1 Clustering as a machine learning task9.4.2 Different types of clustering techniques9.4.3 Partitioning methods9.4.4 K-Medoids: a representative object-based technique9.4.5 Hierarchical clustering9.4.6 Density-based methods - DBSCAN9.5 Finding Pattern using Association Rule9.5.1 Definition of common terms9.5.2 Association rule9.5.3 The apriori algorithm for association rule learning9.5.4 Build the apriori principle rules9.6 Summary
10 Basics of Neural Network
10.1 Introduction10.2 Understanding the Biological Neuron10.3 Exploring the Artificial Neuron10.4 Types of Activation Functions10.4.1 Identity function10.4.2 Threshold/step function10.4.3 ReLU (Rectified Linear Unit) function10.4.4 Sigmoid function10.4.5 Hyperbolic tangent function10.5 Early Implementations of ANN10.5.1 McCulloch–Pitts model of neuron10.5.2 Rosenblatt’s perceptron10.5.3 ADALINE network model10.6 Architectures of Neural Network10.6.1 Single-layer feed forward network10.6.2 Multi-layer feed forward ANNs10.6.3 Competitive network10.6.4 Recurrent network10.7 Learning Process in ANN10.7.1 Number of layers10.7.2 Direction of signal flow10.7.3 Number of nodes in layers10.7.4 Weight of interconnection between neurons10.8 Backpropagation10.9 Deep Learning10.10 Summary
11 Other Types of Learning
11.1 Introduction11.2 Representation Learning11.2.1 Supervised neural networks and multilayer perceptron11.2.2 Independent component analysis (Unsupervised)11.2.3 Autoencoders11.2.4 Various forms of clustering11.3 Active Learning11.3.1 Heuristics for active learning11.3.2 Active learning query strategies11.4 Instance-Based Learning (Memory-based Learning)11.4.1 Radial basis function11.4.2 Pros and cons of instance-based learning method11.5 Association Rule Learning Algorithm11.5.1 Apriori algorithm11.5.2 Eclat algorithm11.6 Ensemble Learning Algorithm11.6.1 Bootstrap aggregation (Bagging)11.6.2 Boosting11.6.3 Gradient boosting machines (GBM)11.7 Regularization Algorithm11.8 Summary
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

Content preview from Machine Learning

Appendix B

Programming Machine Learning in Python

B.1 PRE-REQUISITES

B.1.1 Install anaconda in your system

Before starting to do machine learning programming in Python, please install Python Anaconda distribution from https://anaconda.org/anaconda/python. To write any python script, open any text editor and write your python script and save with the extension .py. You may also use the Spyder (Scientific PYthon Development EnviRonment), which is a powerful and interactive development environment for the Python language with advanced editing, interactive testing, debugging, and introspection features. In this chapter, all the examples have been created working in Spyder.

B.1.2 Know how to manage Python scripts

Open new/pre-existing .py script ...

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Publisher Resources

ISBN: 9789389588132

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

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IT/Ops

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Machine Learning

by Subramanian Chandramouli, Saikat Dutt, Amit Kumar Das

Appendix B