Machine Learning for Text and Image Data Analysis : Practical Approach with Business Use Cases

By:

Motwani Bharti

Material type: Text

TextPublication details: New Delhi Wiley India 2023Description: 805pISBN:

9789354643606

Subject(s):

DDC classification:

006.31 MOT/M

Contents:

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 Adversarial Network

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Previous	006.31 MAN/F Foundations of statistical natural language processing	006.31 MIT/M Machine Learning	006.31 MON/Q QML Unlocked: From Curiosity to Capability in Quantum Machine Learning	006.31 MOT/M Machine Learning for Text and Image Data Analysis : Practical Approach with Business Use Cases	006.31 MUN/A Accelerators for Convolutional Neural Networks	006.31 MUR/P Probabilistic Machine Learning : An Introduction	006.31 POW/R Reinforcement Learning and Stochastic Optimization : A Unified Framework for Sequential Decisions	Next

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 Adversarial Network

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