Machine Learning for Text and Image Data Analysis

Description

Machine Learning for Text and Image Data Analysis: Practical Approach with Business Use Cases enables readers gain sufficient knowledge and experience to perform analysis for text and image data using different machine learning techniques available in Python. The objective is to explain the concepts and to simultaneously develop in readers an understanding of its application with case-based methodology. The book attempts to provide more meaningful and easier learning experience, it has been written with more interesting and relevant real-life examples.

About the Author

Dr. Bharti Motwani has over 25 years of experience in teaching, corporate,

research, and consultancy in a variety of contexts. She is an analytical professional, IT and analytics consultant, result-driven and articulate academician who can think out of the box, who loves to innovate and engage in new challenges. She is an author of many books related to data analytics with Wiley Publishers including Data Analytics With R, HR Analytics: Practical Approach Using Python and Data Analytics Using Python. Her books are accepted as textbooks in IIM and other premium institutions.

Table of Contents

Book Review

Preface

About the Author

Section 1 Introduction to Text and Image Data Analysis

Chapter 1 Basics of Python

1.1 Introduction to Python

1.2 Programming in Python

1.3 Data Structures in Python

1.4 Basic Functions for Text Data

1.5 Data Management

1.6 Data Visualization

Chapter 2 Text and Image Data Pre-Processing

2.1 Text Data Pre-Processing Using nltk Library

2.2 Text Pre-Processing Using “spacy” Library

2.3 Image Data Pre-Processing

Section 2 Unsupervised Machine Learning for Text and Image Data Analysis

Chapter 3 Sentiment Analysis and Topic Modeling

3.1 Introduction

3.2 Sentiment Analysis Using Lexicon-Based Approach

3.3 Topic Modeling Using “Gensim” Library

Chapter 4 Content-Based Recommendation System

4.1 Introduction

4.2 Content-Based Recommendation System for Text Data

4.3 Content-Based Recommendation System for Image Data

Chapter 5 Collaborative Filtering Recommendation System

5.1 Introduction

5.2 Collaborative Filtering Recommendation System for Text Data

5.3 Collaborative Filtering Recommendation System for Image Data

Chapter 6 Association Rule Mining

6.1 Introduction

6.2 Association Rule Mining for Text Data

6.3 Image Data analysis

Chapter 7 Cluster Analysis

7.1 Introduction

7.2 Cluster Analysis for Text Data

7.3 Cluster Analysis for Image Data

Section 3 Supervised Machine Learning for Text and Image Data Analysis

Chapter 8 Supervised Machine Learning Problems

8.1 Introduction

8.2 Supervised Machine Learning Algorithms for Text Data Analysis

8.3 Supervised Machine Learning Algorithms for Image Data Analysis

Chapter 9 Supervised Machine Learning Regression Techniques

9.1 Introduction

9.2 Supervised Machine Learning Regression Algorithms for Text Data Analysis

9.3 Supervised Machine Learning Regression Algorithms for Image Data Analysis

Chapter 10 Supervised Machine Learning Classification Techniques

10.1 Introduction

10.2 Supervised Machine Learning Classification Algorithms for Text Data Analysis

10.3 Supervised Machine Learning Classification Algorithms for Image Data Analysis

Section 4 Deep Learning for Text and Image Data Analysis

Chapter 11 Neural Network Models (Deep Learning)

11.1 Introduction

11.2 Neural Network Models for Text Data Analysis

11.3 Neural Network Models for Image Data Analysis

Chapter 12 Transfer Learning for Text Data Analysis

12.1 Introduction

12.2 Recommendation System Using Transfer Learning for Text Data

12.3 Cluster Analysis Using Transfer Learning for Text Data

12.4 Supervised Machine Learning Using Transfer Learning for Text Data Analysis

12.5 User-Defined Trained Deep Learning Model

12.6 Text Data Extraction Using Transfer Learning for Text Data

Chapter 13 Transfer Learning for Image Data Analysis

13.1 Introduction

13.2 Recommendation System Using Transfer Learning for Image Data

13.3 Cluster Analysis Using Transfer Learning for Image Data

13.4 Supervised Machine Learning Using Transfer Learning for Image Data Analysis

13.5 Facial Recognition Using Transfer Learning for Image Data Analysis

13.6 Gender and Age Determination Using Transfer Learning for Image Data Analysis

13.7 Creating, Saving, and Loading User-Defined Model for Feature Extraction

Chapter 14 Chatbots with Rasa

14.1 Understanding Rasa Environment and Executing Default Chatbot

14.2 Basic Chatbot

14.3 Chatbot with Entities and Actions

14.4 Chatbot with Slots

14.5 Creating Chatbot with Database

14.6 Chatbot with Forms

14.7 Creating Effective Chatbot

Chapter 15 The Road Ahead

15.1 Reinforcement Learning

15.2 Federated Learning

15.3 Graph Neural Networks

15.4 Generative Adversial Network

Answer Keys to the Multiple-Choice Questions

Possible Interview Questions

Index