Machine Learning for Industrial Applications

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 h...

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Detalles Bibliográficos
Autor principal:	Prakash, Kolla Bhanu (-)
Formato:	Libro electrónico
Idioma:	Inglés
Publicado:	Newark : John Wiley & Sons, Incorporated 2024.
Edición:	1st ed
Colección:	Next-generation computing and communication engineering
Materias:	Machine learning > Industrial applications.
Ver en Biblioteca Universitat Ramon Llull:	https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009847336806719

Tabla de Contenidos:

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
References
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
References
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
References
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
References
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
References
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
References
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
References
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
References
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?.

Machine Learning for Industrial Applications

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