Machine Learning for Industrial Applications.

Format:

Book

Author/Creator:

Prakash, Kolla Bhanu.

Series:

Next-generation computing and communication engineering

Language:

English

Subjects (All):

Machine learning--Industrial applications.

Machine learning.

Physical Description:

1 online resource (341 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2024.

Summary:

The main goal of the book is to provide a comprehensive and accessible guide that empowers readers to understand, apply, and leverage machine learning algorithms and techniques effectively in real-world scenarios. Welcome to the exciting world of machine learning! In recent years, machine learning has rapidly transformed from a niche field within computer science to a fundamental technology shaping various aspects of our lives. Whether you realize it or not, machine learning algorithms are at work behind the scenes, powering recommendation systems, autonomous vehicles, virtual assistants, medical diagnostics, and much more. This book is designed to serve as your comprehensive guide to understanding the principles, algorithms, and applications of machine learning. Whether a student diving into this field for the first time, a seasoned professional looking to broaden your skillset, or an enthusiast eager to explore cutting-edge advancements, this book has something for you. The primary goal of Machine Learning for Industrial Applications is to demystify machine learning and make it accessible to a wide audience. It provides a solid foundation in the fundamental concepts of machine learning, covering both the theoretical underpinnings and practical applications. Whether you're interested in supervised learning, unsupervised learning, reinforcement learning, or innovative techniques like deep learning, you'll find comprehensive coverage here. Throughout the book, a hands-on approach is emphasized. As the best way to learn machine learning is by doing, the book includes numerous examples, exercises, and real-world case studies to reinforce your understanding and practical skills. Audience The book will enjoy a wide readership as it will appeal to all researchers, students, and technology enthusiasts wanting a hands-on guide to the new advances in machine learning.

Contents:

Cover

Series Page

Title Page

Copyright Page

Dedication Page

Contents

Preface

Chapter 1 Overview of Machine Learning

1.1 Introduction

1.2 Sorts of Machine Learning

1.3 Regulated Gaining Knowledge of Dog and Human

1.4 Solo Learning

1.5 Support Mastering

1.6 Bundles or Applications of Machine Learning

1.6.1 Photograph Reputation

1.6.2 Discourse Recognition

1.6.3 Traffic Prediction

1.6.4 Item Recommendations

1.6.5 Self-Using Vehicles

1.6.6 Electronic Mail Unsolicited Mail And Malware Filtering

1.6.7 Computerized Private Assistant

1.6.8 Online Fraud Detection

1.6.9 Securities Exchange Buying and Selling

1.6.10 Clinical Prognosis

1.6.11 Computerized Language Translation

1.6.12 Online Media Features

1.6.13 Feeling Evaluation

1.6.14 Robotizing Employee Get Right of Entry to Manipulate

1.6.15 Marine Flora and Fauna Protection

1.6.16 Anticipate Potential Coronary Heart Failure

1.6.17 Directing Healthcare Efficiency and Scientific Offerings

1.6.18 Transportation and Commuting (Uber)

1.6.19 Dynamic Pricing

1.6.19.1 How Does Uber Decide the Cost of Your Excursion?

1.6.20 Online Video Streaming (Netflix)

1.7 Challenges in Machine Learning

1.8 Limitations of Machine Learning

1.9 Projects in Machine Learning

References

Chapter 2 Machine Learning Building Blocks

2.1 Data Collection

2.1.1 Importing the Data from CSV Files

2.2 Data Preparation

2.2.1 Data Exploration

2.2.2 Data Pre-Processing

2.3 Data Wrangling

2.4 Data Analysis

2.5 Model Selection

2.6 Model Building

2.7 Model Evaluation

2.7.1 Classification Metrics

2.7.1.1 Accuracy

2.7.1.2 Precision

2.7.1.3 Recall

2.7.2 Regression Metrics

2.7.2.1 Mean Squared Error

2.7.2.2 Root Mean Squared Error

2.7.2.3 Mean Absolute Error

2.8 Deployment.

2.8.1 Machine Learning Projects

2.8.2 Spam Detection Using Machine Learning

2.8.3 Spam Detection for YouTube Comments Using Naïve Bayes Classifier

2.8.4 Fake News Detection

2.8.5 House Price Prediction

2.8.6 Gold Price Prediction

Bibliography

Chapter 3 Multilayer Perceptron (in Neural Networks)

3.1 Multilayer Perceptron for Digit Classification

3.1.1 Implementation of MLP using TensorFlow for Classifying Image Data

3.2 Training Multilayer Perceptron

3.3 Backpropagation

Chapter 4 Kernel Machines

4.1 Different Kernels and Their Applications

4.2 Some Other Kernel Functions

4.2.1 Gaussian Radial Basis Function (RBF)

4.2.2 Laplace RBF Kernel

4.2.3 Hyperbolic Tangent Kernel

4.2.4 Bessel Function of the First-Kind Kernel

4.2.5 ANOVA Radial Basis Kernel

4.2.6 Linear Splines Kernel in One Dimension

4.2.7 Exponential Kernel

4.2.8 Kernels in Support Vector Machine

Chapter 5 Linear and Rule-Based Models

5.1 Least Squares Methods

5.2 The Perceptron

5.2.1 Bias

5.2.2 Perceptron Weighted Sum

5.2.3 Activation Function

5.2.3.1 Types of Activation Functions

5.2.4 Perceptron Training

5.2.5 Online Learning

5.2.6 Perceptron Training Error

5.3 Support Vector Machines

5.4 Linearity with Kernel Methods

Chapter 6 Distance-Based Models

6.1 Introduction

6.1.1 Distance-Based Clustering

6.2 K-Means Algorithm

6.2.1 K-Means Algorithm Working Process

6.3 Elbow Method

6.4 K-Median

6.4.1 Algorithm

6.5 K-Medoids, PAM (Partitioning Around Medoids)

6.5.1 Advantages

6.5.2 Drawbacks

6.5.3 Algorithm

6.6 CLARA (Clustering Large Applications)

6.6.1 Advantages

6.6.2 Disadvantages

6.7 CLARANS (Clustering Large Applications Based on Randomized Search)

6.7.1 Advantages

6.7.2 Disadvantages.

6.7.3 Algorithm

6.8 Hierarchical Clustering

6.9 Agglomerative Nesting Hierarchical Clustering (AGNES)

6.10 DIANA

Chapter 7 Model Ensembles

7.1 Bagging

7.1.1 Advantages

7.1.2 Disadvantages

7.1.3 Bagging Workage

7.1.4 Algorithm

7.2 Boosting

7.2.1 Types of Boosting

7.2.2 Advantages

7.2.3 Disadvantages

7.2.4 Algorithm

7.3 Stacking

7.3.1 Architecture of Stacking

7.3.2 Stacking Ensemble Family

Chapter 8 Binary and Beyond Binary Classification

8.1 Binary Classification

8.2 Logistic Regression

8.3 Support Vector Machine

8.4 Estimating Class Probabilities

8.5 Confusion Matrix

8.6 Beyond Binary Classification

8.7 Multi-Class Classification

8.8 Multi-Label Classification

Reference

Chapter 9 Model Selection

9.1 Model Selection Considerations

9.1.1 What Do We Care Approximately When Choosing the Final Version?

9.2 Model Selection Strategies

9.3 Types of Model Selection

9.3.1 Methods of Re-Sampling

9.3.2 Random Separation

9.3.3 Time Divide

9.3.4 K-Fold Cross-Validation

9.3.5 Stratified K-Fold

9.3.6 Bootstrap

9.3.7 Possible Steps

9.3.8 Akaike Information Criterion (AIC)

9.3.9 Bayesian Information Criterion (BIC)

9.3.10 Minimum Definition Length (MDL)

9.3.11 Building Risk Reduction (SRM)

9.3.12 Excessive Installation (Overfitting)

9.4 The Principle of Parsimony

9.5 Examples of Model Selection Criterions

9.6 Other Popular Properties

9.7 Key Considerations

9.8 Model Validation

9.8.1 Why is Model Validation Important?

9.8.2 How to Validate the Model

9.8.3 What is a Model Validation Test?

9.8.4 Benefits of Modeling Validation

9.8.5 Model Validation Traps

9.8.6 Data Verification

9.8.7 Model Performance and Validation

9.9 Self-Driving Cars

9.10 K-Fold Cross Validation.

9.11 No One-Size-Fits-All Model Validation

9.12 Validation Strategies

9.13 K-Fold Cross-Validation

9.14 Capture Confirmation Using Hold-Out Validation

9.15 Comparison of Validation Strategy

Chapter 10 Support Vector Machines

10.1 History

10.2 Model

10.3 Kinds of Support Vector Machine

10.3.1 Straight SVM

10.3.2 Non-Direct SVM

10.3.3 Benefits of Help Vector Machines

10.3.4 The Negative Marks of Help Vector Machines

10.3.5 Applications

10.4 Hyperplane and Support Vectors Inside the SVM Set of Rules

10.4.1 Hyperplane

10.5 Support Vectors

10.6 SVM Kernel

10.7 How Can It Function?

10.7.1 See the Right Hyperplane (Circumstance 1)

10.7.2 See the Appropriate Hyperplane (Situation 2)

10.7.3 Distinguish the Right Hyper-Airplane (Situation 3)

10.7.4 Would We Have the Option to Organize Models (Circumstance 4)?

10.7.5 Track Down the Hyperplane to Isolate Into Guidelines (Situation 5)

10.8 SVM for Classification

10.9 SVM for Regression

10.10 Python Implementation of Support Vector Machine

10.10.1 Data Pre-Taking Care of Step

10.10.2 Fitting the SVM Classifier to the Readiness Set

10.10.2.1 Outcome

10.10.3 Anticipating the Investigated Set Final Product

10.10.3.1 Yield

10.10.4 Fostering the Disarray Lattice

10.10.5 Picturing the Preparation Set Outcome

10.10.5.1 Yield

10.10.6 Imagining the Investigated Set Outcome

10.10.6.1 Yield

10.10.7 Part or Kernel

10.10.8 Support Vector Machine (SVM) Code in Python

10.10.9 Intricacy of SVM

Chapter 11 Clustering

11.1 Example

11.2 Kinds of Clustering

11.2.1 Hard Clustering

11.2.2 Delicate Clustering

11.2.2.1 Dividing Clustering or Partitioning Clustering

11.2.2.2 Thickness Essentially Based Clustering or Density Fundamentally Based Clustering.

11.2.2.3 Transport Model-Based Clustering or Distribution Model-Based Clustering

11.2.2.4 Progressive Clustering or Hierarchical Clustering

11.2.2.5 Fluffy Clustering or Fuzzy Clustering

11.3 What are the Utilization of Clustering?

11.4 Models

11.5 Uses of Clustering

11.5.1 In Character of Most Tumor Cells

11.5.2 In Web Crawlers Like Google

11.5.3 Shopper Segmentation

11.5.4 In Biology

11.5.5 In Land Use

11.6 Bunching Algorithms or Clustering Algorithms

11.6.1 K-Means Clustering

11.6.2 Mean-Shift Clustering

11.6.3 Thickness or Density-Based Spatial Clustering of Application with Noise (DBSCAN)

11.6.4 Assumption Maximization Clustering Utilizing Gaussian Combination Models

11.6.5 Agglomerative Hierarchical Clustering

11.7 Instances of Clustering Algorithms

11.7.1 Library Setup

11.7.2 Grouping or Clustering Dataset

11.7.3 Fondness or Affinity Propagation

11.7.4 Agglomerative Clustering

11.7.5 BIRCH

11.7.6 DBSCAN

11.7.7 K-Means

11.7.8 Mini-Batch K-Means

11.7.9 Mean Shift

11.7.10 OPTICS

11.7.11 Unearthly or Spectral Clustering

11.7.12 Gaussian Mixture Model

11.8 Python Implementation of K-Means

11.8.1 Stacking the Data

11.8.2 Plotting the Information

11.8.3 Choosing the Component

11.8.4 Clustering

11.8.5 Clustering Results

11.8.6 WCSS and Elbow Technique

11.8.7 Uses of K-Mean Bunching

11.8.8 Benefits of K-Means

11.8.9 Bad Marks of K-MEAN

Chapter 12 Reinforcement Learning

12.1 Model

12.2 Terms Utilized in Reinforcement Learning

12.3 Key Elements of Reinforcement Learning

12.4 Instances of Reinforcement Learning

12.5 Advantages of Reinforcement Learning

12.6 Challenges with Reinforcement Learning

12.7 Sorts of Reinforcement

12.7.1 Positive

12.7.2 Negative.

12.8 What are the Useful Utilizations of Reinforcement Learning?.

Notes:

Includes index.

Description based on publisher supplied metadata and other sources.

ISBN:

9781394268993

1394268998

9781394268986

139426898X

OCLC:

1449623589