Machine Learning and Deep Learning Algorithms
US$ 19.95
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Description
Contents
Reviews
Language
English
ISBN
9789388511131
Cover Page
Title Page
Copyright Page
Preface
Foreword
Acknowledgement
Authors
Table of Contents
Pre-requisite to Machine Learning
1. Accessing the Data
2. Pre - processing Data
3. Deriving Data/ Missing Data in MATLAB
4. Importing and Organizing Data
4.1 Data Types
4.2 Categorical Data Plot
4.3 Create and Work with Tables
4.4 Cross Validation
4.5 What is Data Preparation
1. An Introduction to Machine Learning
1.1 Basics of Machine Learning
1.2 Machine Learning Types
1.3 Selection of Appropriate Algorithm
1.4 Linear Programming Algorithms
1.4.1 Steps the Machine Learning Workflow using a Health Monitoring
2. Finding Natural Patterns in Data
2.1 Unsupervised Learning
2.2 Clustering Strategies
2.2.1 Hard Clustering Calculations
2.2.2 Soft Clustering Calculations
2.3 Cluster Evaluation and Interpretation
2.3.1 Common Dimensionality Reduction Techniques for Improving Model Performance
3. Building Classification Methods
3.1 Supervised Learning
3.2 Supervised Machine Learning
3.3 Unsupervised Machine Learning
3.4 Semi-supervised Learning
3.4.1 Understanding Semi-supervised Learning
3.5 Reinforcement Learning
3.6 Some Important Consideration in Machine Learning
3.7 Training and Validation
3.8 Classification of Methods
3.8.1 Training of Automated Classifier
3.8.2 Manual Classifier Training
3.8.3 Parallel Classifier Training
3.9 Algorithm for Classification
3.9.1 Classification Algorithm in General
3.9.2 Common Classification Algorithm
3.9.3 Regression
3.9.4 Regression Algorithms
3.10 Techniques for Model Improvement
3.10.1 Selecting Features for Classifying High-dimensional Data
3.10.2 Loading the Data
3.10.3 Sequential Feature Selection Application
4. Data Pre – Processing in Python
4.1 Data Preparation
4.1.1 Data Preparation Process
4.2 Feature Selection for Machine Learning
4.3 Recursive Feature Elimination
4.4 Principal Component Analysis
4.5 Feature Importance
4.6 Feature Scaling
4.7 Seven Ways to Handle Large Data Files for Machine Learning
4.8 Dimensionality Reduction
4.9 Cross Validation
4.10 Feature Transformation
5. Building Regression Models
5.1 Parametric regression Methods
5.2 Nonparametric Machine Learning Algorithms
5.3 Evaluation of Regression Models
6. Creating Neural Networks
6.1 Self-organizing the Maps and their use in Obtaining K-Clusters
6.2 Classification with Feed-Forward Networks
6.3 Regression with Feed-forward Networks
7. Introduction to Deep Learning
7.1 Deep Learning Overview
7.2 How Deep Learning Works
7.2.1 How is Deep Learning Different from Machine Learning?
7.2.2 Is Deep Learning Different from AI (artificial intelligence)?
7.2.3 What is Deep Learning Framework?
7.2.4 What are the Dimensions of the Deep Learning?
7.3 Deep Learning uses and Functioning
7.4 Programming Languages used to Program (design) Deep Learning?
7.5 Meaning and importance of Deep Learning
7.6 What Deep Learning can do in Future?
7.7 Applications of Deep Learning in Artificial Intelligence
7.8 Fields were deep learning boom:
7.9 The future of deep learning
7.10 Algorithms in Deep Learning
7.11 Comparison of Machine Learning and Deep Learning
7.11.1 Data Dependencies
7.11.2 Hardware dependencies
7.11.3 Execution time
7.11.4 Interpretability
7.12 TensorFlow
7.12.1 What is TensorFlow
7.12.2 Steps to install TensorFlow
7.12.3 Linear Regression with TensorFlow
7.13 Artificial Neural Networks
7.13.1 Neurons
7.13.2 How will Artificial Neural Network Work?
7.13.3 Neuron Weights
7.13.4 Feed-forward Deep Networks
7.13.5 Feed-forward Deep Networks
7.14 Activation function
7.14.1 Back propagation
7.14.2 Cost Perform and Gradient Descent
7.15 Multi-layer perceptron (forward propagation)
7.16 Using Activation Perform
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$19.95
