Your first step toward Machine Learning!

Course Curriculum:

Section 01: Introduction

• Introduction to Supervised Machine Learning

Section 02: Regression

• Introduction to Regression
• Evaluating Regression Models
• Conditions for Using Regression Models in ML versus in Classical Statistics
• Statistically Significant Predictors
• Regression Models Including Categorical Predictors. Additive Effects
• Regression Models Including Categorical Predictors. Interaction Effects

Section 03: Predictors

• Multicollinearity among Predictors and its Consequences
• Prediction for New Observation. Confidence Interval and Prediction Interval
• Model Building. What if the Regression Equation Contains “Wrong” Predictors?

Section 04: Minitab

• Stepwise Regression and its Use for Finding the Optimal Model in Minitab
• Regression with Minitab. Example. Auto-mpg: Part 1
• Regression with Minitab. Example. Auto-mpg: Part 2

Section 05: Regression Trees

• The Basic idea of Regression Trees
• Regression Trees with Minitab. Example. Bike Sharing: Part 1
• Regression Trees with Minitab. Example. Bike Sharing: Part 2

Section 06: Binary Logistics Regression

• Introduction to Binary Logistics Regression
• Evaluating Binary Classification Models. Goodness of Fit Metrics. ROC Curve. AUC
• Binary Logistic Regression with Minitab. Example. Heart Failure: Part 1
• Binary Logistic Regression with Minitab. Example. Heart Failure: Part 2

Section 07: Classification Trees

• Introduction to Classification Trees
• Node Splitting Methods 1. Splitting by Misclassification Rate
• Node Splitting Methods 2. Splitting by Gini Impurity or Entropy
• Predicted Class for a Node
• The Goodness of the Model – 1. Model Misclassification Cost
• The Goodness of the Model – 2 ROC. Gain. Lit Binary Classification
• The Goodness of the Model – 3. ROC. Gain. Lit. Multinomial Classification
• Predefined Prior Probabilities and Input Misclassification Costs
• Building the Tree
• Classification Trees with Minitab. Example. Maintenance of Machines: Part 1
• Classification Trees with Miitab. Example. Maintenance of Machines: Part 2

Section 08: Data Cleaning

• Data Cleaning: Part 1
• Data Cleaning: Part 2
• Creating New Features

Section 09: Data Models

• Polynomial Regression Models for Quantitative Predictor Variables
• Interactions Regression Models for Quantitative Predictor Variables
• Qualitative and Quantitative Predictors: Interaction Models
• Final Models for Duration and TotalCharge: Without Validation
• Underfitting or Overfitting: The “Just Right Model”
• The “Just Right” Model for Duration
• The “Just Right” Model for Duration: A More Detailed Error Analysis
• The “Just Right” Model for TotalCharge
• The “Just Right” Model for ToralCharge: A More Detailed Error Analysis

Section 10: Learning Success

• Regression Trees for Duration and TotalCharge
• Predicting Learning Success: The Problem Statement
• Predicting Learning Success: Binary Logistic Regression Models
• Predicting Learning Success: Classification Tree Models

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