Machine Learning with Minitab Predictive Analytics

🚀 Course Title: Machine Learning Basics with Minitab

🎓 Course Headline: Setting up and Evaluating Regression and Classification Models with Elaborated Examples and Minitab Tutorials

Introduction to Machine Learning with Minitab 🧠

Welcome to the world of Machine Learning where you'll uncover the power of predictive analytics using Minitab! This course is meticulously designed for learners who are eager to grasp the foundational concepts and practical applications of machine learning, specifically focusing on supervised learning.

Course Description:

This comprehensive course will take you on a journey through the landscapes of regression analysis and binary logistic classification. You'll delve into how to set up, evaluate, and interpret models within the context of machine learning. The course is structured to provide you with both the theoretical underpinnings and hands-on practice with Minitab for tree-based models in binary and multinomial classification.

Module 1: Laying the Groundwork for Machine Learning with Minitab 🏗️

• What is Machine Learning? Dive into understanding the scope, types, and the difference between supervised and unsupervised learning.
• Supervised Learning Basics: Get acquainted with the various types of regression models and the prerequisites for using them in machine learning.

Module 2: Mastering Regression Analysis with Minitab 📊

• Types of Regression Models: Explore different models and understand how to apply them using Minitab.
• Statistical Significance & Multicollinearity: Learn the nuances of interpreting regression models, especially when dealing with categorical predictors and their effects.
• Predicting Outcomes: Gain insights into making predictions for new observations using confidence and prediction intervals.

Module 3: Model Building & Evaluation Techniques 🔍

• Effective Model Selection: Discover how to identify and remove "wrong" predictors through stepwise regression to achieve optimal models.
• Model Evaluation & Interpretation: Master the art of evaluating models with metrics such as R-squared, F-test, ROC curve, and AUC for binary classification problems.

Module 4: Binary Logistic Regression - The Art of Predicting Categories 🎲

• Binary Classification Models: Understand the mechanics of binary logistic regression and its application in real-world scenarios.
• Evaluating Binary Classifiers: Learn how to assess the performance of your models using ROC curve, AUC, and other good fit metrics.
• Practical Application: Work with a heart failure dataset to apply binary logistic regression techniques using Minitab.

Module 5: Tree-Based Classification Models 🌳

• Classification Trees: Explore the different methods for splitting nodes in classification trees, including misclassification rate, Gini impurity, and entropy.
• Model Evaluation for Classification Trees: Learn how to use misclassification costs, ROC curve, Gain chart, and Lift chart for both binary and multinomial classification.
• Applying Prior Probabilities and Costs: Understand the importance of predefined prior probabilities and input misclassification costs in building a robust tree model with Minitab.

Capstone Project: Applying Your Skills 🏆

Throughout the course, you'll engage in practical exercises that apply the concepts learned. The culmination of your learning journey will be a capstone project where you'll demonstrate your ability to set up and evaluate machine learning models using Minitab on real-world data problems.

By completing this course, you'll have a thorough understanding of machine learning basics with Minitab, equipped with the knowledge and skills necessary to tackle a variety of data analysis challenges in the field of supervised learning, regression analysis, and classification. 🌟

Enroll now and embark on your journey to becoming a proficient machine learning practitioner with Minitab! 📆