●Preface
Chapter 1:Machine Learning Review
Machine learning-history and definition
What is not machine learning?
Machine learning-concepts and terminology
Machine learning-types and subtypes
Datasets used in machine learning
Machine learning applications
Practical issues in machine learning
Machine learning-roles and process
Roles
Process
Machine learning-tools and datasets
Datasets
Summary
Chapter 2:Practical Approach to Real-World Supervised Learning
Formal description and notation
Data quality analysis
Descriptive data analysis
Basic label analysis
Basic feature analysis
Visualization analysis
Univariate feature analysis
Multivariate feature analysis
Data transformation and preprocessing
Feature construction
Handling missing values
Outliers
Discretization
Data sampling
Is sampling needed?
Undersampling and oversampling
Training, validation, and test set
Feature relevance analysis and dimensionality reduction
Feature search techniques
Feature evaluation techniques
Filter approach
Wrapper approach
Embedded approach
Model building
Linear models
Linear Regression
Naive Bayes
Logistic Regression
Non-linear models
Decision Trees
K-Nearest Neighbors(KNN)
Support vector machines(SVM)
Ensemble learning and meta learners
Bootstrap aggregating or bagging
Boosting
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