Machine Learning with Minitab Predictive Analytics

Machine Learning with Minitab Predictive Analytics, available at $59.99, has an average rating of 4.55, with 48 lectures, 4 quizzes, based on 15 reviews, and has 77 subscribers.

You will learn about You will learn the fundamentals of machine learning with a focus on practical applications using Minitab. You will also learn how to apply these techniques to real world problems in a wide variety of application areas. This hands-on approach will give you the confidence and skills you need to succeed in a career in data analysis or machine learning. By the end of the course, you'll be able to build and implement regression and classification models and gain a deep understanding of their underlying concepts. This course is ideal for individuals who are This course is designed for students with a basic statistics background who are new to machine learning and want to gain practical skills in this field. No programming experience is necessary, but the course will introduce you to the advanced use of Minitab's menu-driven interface. Machine Learning is a multi-disciplinary field, often only to be learned in more depth over several books and courses, but this course is the perfect first learning resource. It is particularly useful for This course is designed for students with a basic statistics background who are new to machine learning and want to gain practical skills in this field. No programming experience is necessary, but the course will introduce you to the advanced use of Minitab's menu-driven interface. Machine Learning is a multi-disciplinary field, often only to be learned in more depth over several books and courses, but this course is the perfect first learning resource.

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Summary

Title: Machine Learning with Minitab Predictive Analytics

Price: $59.99

Average Rating: 4.55

Number of Lectures: 48

Number of Quizzes: 4

Number of Published Lectures: 48

Number of Published Quizzes: 4

Number of Curriculum Items: 59

Number of Published Curriculum Objects: 59

Number of Practice Tests: 2

Number of Published Practice Tests: 2

Original Price: $59.99

Quality Status: approved

Status: Live

What You Will Learn

You will learn the fundamentals of machine learning with a focus on practical applications using Minitab.
You will also learn how to apply these techniques to real world problems in a wide variety of application areas.
This hands-on approach will give you the confidence and skills you need to succeed in a career in data analysis or machine learning.
By the end of the course, you'll be able to build and implement regression and classification models and gain a deep understanding of their underlying concepts.

Who Should Attend

This course is designed for students with a basic statistics background who are new to machine learning and want to gain practical skills in this field. No programming experience is necessary, but the course will introduce you to the advanced use of Minitab's menu-driven interface. Machine Learning is a multi-disciplinary field, often only to be learned in more depth over several books and courses, but this course is the perfect first learning resource.

Target Audiences

This course is designed for students with a basic statistics background who are new to machine learning and want to gain practical skills in this field. No programming experience is necessary, but the course will introduce you to the advanced use of Minitab's menu-driven interface. Machine Learning is a multi-disciplinary field, often only to be learned in more depth over several books and courses, but this course is the perfect first learning resource.

Course Title: Machine Learning Basics with Minitab

Course Description:

This comprehensive course is designed to provide a detailed understanding of the basics of machine learning using Minitab, with a focus on supervised learning. The course covers the fundamental concepts of regression analysis and binary logistic classification, including how to evaluate models and interpret results. The course also covers tree-based models for binary and multinomial classification.

The course begins with an introduction to machine learning, where students will gain an understanding of what machine learning is, the different types of machine learning, and the difference between supervised and unsupervised learning. This is followed by an overview of the basics of supervised learning, including how to learn, the different types of regression, and the conditions that must be met to use regression models in machine learning versus classical statistics.

The course then delves into regression analysis in detail, covering the different types of regression models and how to use Minitab to evaluate them. This includes a thorough explanation of statistically significant predictors, multicollinearity, and how to handle regression models that include categorical predictors, including additive and interaction effects. Students will also learn how to make predictions for new observations using confidence intervals and prediction intervals.

Next, the course moves onto model building, where students will learn how to handle regression equations with “wrong” predictors and use stepwise regression to find optimal models in Minitab. This includes an overview of how to evaluate models and interpret results.

The course then shifts to binary logistic regression, which is used for binary classification. Students will learn how to evaluate binary classification models, including good fit metrics such as the ROC curve and AUC. They will also use Minitab to analyze a heart failure dataset using binary logistic regression.

The course then covers classification trees, including an overview of node splitting methods such as splitting by misclassification rate, Gini impurity, and entropy. Students will learn how to predict class for a node and evaluate the goodness of the model using misclassification costs, ROC curve, Gain chart, and Lift chart for both binary and multinomial classification.

Finally, the course covers the concept and use of predefined prior probabilities and input misclassification costs, and how to build a tree using Minitab. Throughout the course, students will gain hands-on experience applying the concepts learned in real-world scenarios.

Overall, this course provides a thorough understanding of machine learning basics using Minitab, with a focus on supervised learning, regression analysis, and classification. Upon completion of this course, students will have the knowledge and skills to apply supervised machine learning techniques to real-world data problems.

Course Curriculum

Chapter 1: Introduction to Supervised Machine Learning

Lecture 1: Introduction to Supervised Machine Learning

Chapter 2: Regression in Classical Statistics and in Machine Learning (ML)

Lecture 1: Introduction to Regression

Lecture 2: Evaluating Regression Models

Lecture 3: Conditions for Using Regression Models in ML versus in Classical Statistics

Lecture 4: Model Building. What if the Regression Equation Contains "Wrong" Predictors?

Lecture 5: Statistically Significant Predictors

Lecture 6: Regression Models Including Categorical Predictors. Additive Effects

Lecture 7: Regression Models Including Categorical Predictors. Interaction Effects

Lecture 8: Multicollinearity among Predictors and its Consequences

Lecture 9: Prediction for New Observation. Confidence Interval and Prediction Interval

Lecture 10: Stepwise Regression and its Use for Finding the Optimal Model in Minitab

Chapter 3: Regression with Minitab. Examples and Exercises

Lecture 1: Regression with Minitab. Example. Auto-mpg. Part 1

Lecture 2: Regression with Minitab. Example. Auto-mpg. Part 2

Chapter 4: Regression Tree Models

Lecture 1: The Basic Idea of Regression Trees

Chapter 5: Regression Trees with Minitab. Examples and Exercises

Lecture 1: Regression Trees with Minitab. Example. Bike Sharing. Part 1

Lecture 2: Regression Trees with Minitab. Example. Bike Sharing. Part 2

Chapter 6: Classification by Binary Logistic Regression Models

Lecture 1: Introduction to Binary Logistic Regression

Lecture 2: Evaluating Binary Classification Models. Goodness of Fit Metrics. ROC Curve. AUC

Chapter 7: Binary Logistic Regression Models with Minitab. Examples and Exercises

Lecture 1: Binary Logistic Regression with Minitab. Example. Heart Failure. Part 1

Lecture 2: Binary Logistic Regression with Minitab. Example. Heart Failure. Part 2

Chapter 8: Classification Tree Models

Lecture 1: Introduction to Classification Trees

Lecture 2: Node Splitting Methods 1. Splitting by Misclassification Rate

Lecture 3: Node Splitting Methods 2. Splitting by Gini Impurity or Entropy

Lecture 4: Predicted Class for a Node

Lecture 5: The Goodness of the Model – 1. Model Misclassification Cost

Lecture 6: The Goodness of the Model – 2. ROC. Gain. Lift. Binary Classification

Lecture 7: The Goodness of the Model – 3. ROC. Gain. Lift. Multinomial Classification

Lecture 8: Predefined Prior Probabilities and Input Misclassification Costs

Lecture 9: Building the Tree

Chapter 9: Classification Tree Models. Examples and Exercises with Minitab

Lecture 1: Classification Trees with Minitab. Example. Maintenance of Machines. Part 1

Lecture 2: Classification Trees with Minitab. Example. Maintenance of Machines. Part 2

Chapter 10: Comprehensive Project 1. Regression Models for New York Yellow Taxi Trips

Lecture 1: Data Cleaning. Part 1

Lecture 2: Data Cleaning. Part 2

Lecture 3: Creating New Features

Lecture 4: Polynomial Regression Models for Quantitative Predictor Variables

Lecture 5: Interactions Regression Models for Quantitative Predictor Variables

Lecture 6: Qualitative and Quantitative Predictors. Interaction Models

Lecture 7: Final Models for Duration and TotalCharge. Without Validation

Lecture 8: Underfitting or Overfitting. The "Just Right" Model

Lecture 9: The "Just Right" Model for Duration

Lecture 10: The "Just Right" Model for Duration. A More Detailed Error Analysis

Lecture 11: The "Just Right" Model for TotalCharge

Lecture 12: The "Just Right" Model for TotalCharge. A More Detailed Error Analysis

Lecture 13: Regression Trees for Duration and TotalCharge

Chapter 11: Comprehensive Project 2. Classification Models for Predicting Learning Success

Lecture 1: Predicting Learning Success. The Problem Statement

Lecture 2: Predicting Learning Success. Binary Logistic Regression Models

Lecture 3: Predicting Learning Success. Classification Tree Models

Chapter 12: Open Access Machine Learning Repository for Self-study and Self-practice

Lecture 1: Open Access Machine Learning Repository for Self-study and Self-practice

Instructors

László Bognár
Professor of Applied Statistics

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3 stars: 1 votes
4 stars: 4 votes
5 stars: 10 votes

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