Chapter 1.
Business Data Analytics and JMP
1.1 Business Data Analysis
1.2 The intersection of machine learning and statistical approaches and the use of JMP
1.3 JMP Interface and Analysis Environment

Chapter 2.
Understanding the overall flow of machine learning
2.1 Overall flow and key terms of machine learning
2.2 Data Types and Preprocessing
2.3 Variable Selection and Feature Engineering
2.4 Model Performance Comparison Indicators

Chapter 3.
Supervised Learning I? Prediction Using Regression Analysis
3.1 Simple regression analysis
3.2 Multiple Regression Model: Fit Model
3.3 Logistic Regression Analysis
3.4 Generalized Regression Analysis (Variable Selection Method)
3.5 Stepwise Regression Analysis

Chapter 4.
Supervised Learning II? Decision Trees
4.1 Decision Tree (Partition)
4.2 Bootstrap Forest
4.3 Boosted Tree

Chapter 5.
Supervised Learning III? Artificial Neural Networks
5.1 Introduction to the Neural Network Platform
5.2 Performance Improvement and Interpretation Strategies for Neural Network Models

Chapter 6.
Unsupervised Learning I? Dimensionality Reduction
6.1 Principal component analysis and factor analysis (PCA and EFA)
6.2 Multidimensional Scaling (MDS)
6.3 Multivariate Embedding

Chapter 7.
Unsupervised Learning II? Clustering Techniques
7.1 Hierarchical Clustering
7.2 K-means Clustering
7.3 Normal Mixture Analysis
7.4 Latent Class Analysis

Chapter 8: Specialized Techniques and Advanced Analysis
8.1 Partial Least Squares
8.2 Structural Equation Model
8.3 Explore Outliers
8.4 Model Screening

References
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