Machine Learning using Python

Description

This book is written to provide a strong foundation in Machine Learning using Python libraries by providing real-life case studies and examples. It covers topics such as Foundations of Machine Learning, Introduction to Python, Descriptive Analytics and Predictive Analytics. Advanced Machine Learning concepts such as decision tree learning, random forest, boosting, recommender systems, and text analytics are covered. The book takes a balanced approach between theoretical understanding and practical applications. All the topics include real-world examples and provide step-by-step approach on how to explore, build, evaluate, and optimize machine learning models.

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About the Author

U Dinesh Kumar is a Professor of Decision Sciences and Information Systems at IIM Bangalore. Dr Dinesh Kumar has published more than 60 research articles in leading academic journals. Twenty Eight of his case studies on Business Analytics based on Indian and multinational organizations such as Aavin Milk Dairy, Apollo Hospitals, Bigbasket, Bollywood, Flipkart.com, Hewlett and Packard, Larsen & Toubro, Mission Hospital, Hindustan Aeronautics Limited, Indian Premier League

Manaranjan Pradhan, an alumnus of IIM Bangalore, has about 20 years of industry experience working on Cloud computing, Big Data & Machine Learning projects. He worked with TCS, HP, and iGATE before becoming a freelance consultant providing consulting and training on Big data, Machine Learning and Deep Learning. He has been teaching Big Data and Machine Learning for 4 years and has trained more than 1000 persons from several large MNCs including EMC, Cisco, Tesco, HP, Goldman Sachs, Software AG and Amadeus.

Table of Contents

Preface
Acknowledgements

1 Introduction To Machine Learning
1.1 Introduction to Analytics and Machine Learning
1.2 Why Machine Learning?
1.3 Framework for Developing Machine Learning Models
1.4 Why Python?
1.5 Python Stack for Data Science
1.6 Getting Started with Anaconda Platform
1.7 Introduction to Python

2 Descriptive Analytics
2.1 Working with DataFrames in Python
2.2 Handling Missing Values
2.3 Exploration of Data using Visualization

3 Probability Distributions And Hypothesis Tests
3.1 Overview
3.2 Probability Theory – Terminology
3.3 Random Variables
3.4 Binomial Distribution
3.5 Poisson Distribution
3.6 Exponential Distribution
3.7 Normal Distribution
3.7.5 Other Important Distributions
3.8 Central Limit Theorem
3.9 Hypothesis Test
3.10 Analysis of Variance (ANOVA)

4 Linear Regression
4.1 Simple Linear Regression
4.2 Steps in Building a Regression Model
4.3 Building Simple Linear Regression Model
4.4 Model Diagnostics
4.5 Multiple Linear Regression

5 Classification Problems
5.1 Classification Overview
5.2 Binary Logistic Regression
5.3 Credit Classification
5.4 Gain Chart and Lift Chart
5.5 Classification Tree (Decision Tree Learning)

6 Advanced Machine Learning
6.1 Overview
6.2 Gradient Descent Algorithm
6.3 Scikit-Learn Library for Machine Learning
6.4 Advanced Regression Models
6.5 Advanced Machine Learning Algorithms

7 Clustering
7.1 Overview
7.2 How Does Clustering Work?
7.3 K-Means Clustering
7.4 Creating Product Segments Using Clustering
7.5 Hierarchical Clustering

8 Forecasting
8.1 Forecasting Overview
8.2 Components of Time-Series Data
8.3 Moving Average
8.4 Decomposing Time Series
8.5 Auto-Regressive Integrated Moving Average Models

9 Recommender Systems
9.1 Overview
9.2 Association Rules (Association Rule Mining)
9.3 Collaborative Filtering
9.4 Using Surprise Library
9.5 Matrix Factorization

10 Text Analytics
10.1 Overview
10.2 Sentiment Classification
10.3 Naïve-Bayes Model for Sentiment Classification
10.4 Using TF-IDF Vectorizer
10.5 Challenges of Text Analytics

Conclusion
Exercises
References
Index