Big Data, IoT, and Machine Learning Tools and Applications.
Saved in:
| Online Access: |
Full text (MCPHS users only) |
|---|---|
| Main Author: | |
| Other Authors: | , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Milton :
Taylor & Francis Group,
2020
|
| Series: | Internet of Everything (IoE) Ser.
|
| Local Note: | ProQuest Ebook Central |
Table of Contents:
- Cover
- Half Title
- Series Page
- Title Page
- Copyright Page
- Table of Contents
- Preface
- Acknowledgement
- Editors
- Contributors
- Section I Applications of Machine Learning
- Chapter 1 Machine Learning Classifiers
- 1.1 Introduction
- 1.2 Machine Learning Overview
- 1.2.1 Steps in Machine Learning
- 1.2.2 Performance Measures for Machine Learning Algorithms
- 1.2.2.1 Confusion Matrix
- 1.3 Machine Learning Approaches
- 1.4 Types of Machine Learning
- 1.4.1 Supervised Learning
- 1.4.2 Unsupervised Learning
- 1.4.3 Semi-Supervised Learning
- 1.4.4 Reinforcement Learning
- 1.5 A Taste of Classification
- 1.5.1 Binary Classification
- 1.5.2 Multiclass Classification
- 1.5.3 Multilabel Classification
- 1.5.4 Linear Classification
- 1.5.5 Non-Linear Classification
- 1.6 Machine Learning Classifiers
- 1.6.1 Python for Machine Learning Classification
- 1.6.2 Decision Tree
- 1.6.2.1 Building a Decision Tree
- 1.6.2.2 Induction
- 1.6.2.3 Best Attribute Selection
- 1.6.2.4 Pruning
- 1.6.3 Random Forests
- 1.6.3.1 Evaluating Random Forest
- 1.6.3.2 Tuning Parameters in Random Forest
- 1.6.3.3 Splitting Rule
- 1.6.4 Support Vector Machine
- 1.6.5 Neural Networks
- 1.6.5.1 Back Propagation Algorithm
- 1.6.6 Logistic Regression
- 1.6.7 k-Nearest Neighbor
- 1.6.7.1 The k-NN Algorithm
- 1.7 Model Selection and Validation
- 1.7.1 Hyperparameter Tuning and Model Selection
- 1.7.2 Bias, Variance and Model Selection
- 1.7.3 Model Validation
- Conclusion
- References
- Chapter 2 Dimension Reduction Techniques
- 2.1 Dimension Reduction
- 2.2 Dimension Reduction Techniques
- 2.2.1 Feature Selection
- 2.2.2 Feature Extraction
- 2.3 Linear Dimension Reduction Techniques
- 2.3.1 Principal Component Analysis
- 2.3.2 Singular Value Decomposition
- 2.3.3 Latent Discriminant Analysis
- 2.3.4 Independent Component Analysis
- 2.3.5 Projection Pursuits
- 2.3.6 Latent Semantic Analysis
- 2.3.7 Locality Preserving Projection
- 2.4 Nonlinear Dimension Reduction Techniques
- 2.4.1 Kernel Principal Component Analysis
- 2.4.2 Isomap
- 2.4.3 Locally Linear Embedding
- 2.4.4 Self Organising Map
- 2.4.5 Learning Vector Quantisation
- 2.4.6 t-Stochastic Neighbor Embedding
- 2.5 Conclusion and Future Directions
- References
- Chapter 3 Reviews Analysis of Apple Store Applications Using Supervised Machine Learning
- 3.1 Introduction
- 3.2 Literature Review
- 3.2.1 Machine Learning Algorithms
- 3.2.2 Feature Extraction Algorithms
- 3.3 Proposed Methodology
- 3.3.1 Data Collection
- 3.3.2 Feature Extraction
- 3.3.3 Data Analysis and Sentiment Analysis
- Text Processing
- 3.3.4 Text Normalisation
- 3.4 Feature Extraction Algorithm
- 3.4.1 CountVectorizer
- 3.4.2 TfidfVectorizer (TF-IDF)
- 3.5 Supervised ML Classification
- 3.6 Experiment Design
- 3.7 Experimental Results and Analysis
