Overview What You Will Learn Curriculum Instructor

Course Overview

A or B? Win or lose? In or out? Classification is all about finding the right answer with the data you’ve got, and this course will help you get there.  

Throughout this course, you will gain a better understanding of what classification is and the types of problems we typically solve. Then, you will dive into one particular technique for classifying data: logistic regression. You will learn how the algorithm works and then apply it using the R and Python programming languages. Along the way, you will gain an understanding of when to use logistic regression, what a good outcome looks like, and what statistical tools are available to ensure that you’re getting the most out of your model. 

By the end of this course, you’ll have a thorough understanding of logistic regression, and you’ll be able to build, train, and test logistic regression models.

The necessary resources for this course are in the "Resources" section of Video 1.1. You can also access them through this direct link - https://github.com/ec-council-learning/Applied-Logistic-Regression

What You Will Learn

  • Understanding what logistic regression is and understanding the kinds of problems that can be solved using it.
  • Learning the math behind logistic regression and comprehending how it operates.
  • Understanding how to utilize tools such as the confusion matrix to determine how well a logistic regression model fits the data.
  • Extending logistic regression to special cases involving multiple classes and ordered sets.
  • Gaining insights on when logistic regression may be the best classification algorithm and when it may be best to move to something else.

Program Curriculum

  • Regression and Classification
  • What is Logistic Regression?
  • Key Assumptions of Logistic Regression
  • $7 Million Cybersecurity Scholarship by EC-Council
  • Chapter 1 Quiz

  • Logits and Log Odds
  • Exploring the Sigmoid Function
  • Building a Loss Function
  • Optimizing the Regression with Gradient Descent
  • Chapter 2 Quiz

  • Reviewing the Scenario
  • Data Preparation in R
  • Model Training and Scoring in R
  • Data Preparation in Python
  • Model Training and Scoring in Python
  • Chapter 3 Quiz

  • Accuracy Is Not Enough: The Problem of Class Imbalance
  • Untangling the Confusion Matrix
  • Obtaining a Confusion Matrix in R and Python
  • Working with the Receiver Operating Characteristic Curve
  • Chapter 4 Quiz

  • Extending Logistic Regression
  • Multinomial Logistic Regression in R
  • Multinomial Logistic Regression in Python
  • Ordinal Logistic Regression in R
  • Ordinal Logistic Regression in Python
  • Chapter 5 Quiz

  • Remember the Assumptions!
  • Calculating Success
  • Alternative Algorithms for Classification
Load more modules

Instructor

Kevin Feasel

Kevin Feasel is a Microsoft Data Platform MVP out of Durham, North Carolina, where he runs a Predictive Analytics team for a major e-commerce company. Kevin has developed and delivered training across the breadth of Azure Synapse Analytics, with a focus on delivering great solutions which fit business needs while maintaining a strong security posture and respecting the customer’s privacy. He is the lead contributor to Curated SQL (https://curatedsql.com), president of the Triangle Area SQL Server Users Group (https://www.meetup.com/tripass), and author of PolyBase Revealed (https://www.apress.com/us/book/9781484254608). A resident of Durham, North Carolina, he can be found cycling the trails along the triangle whenever the weather's nice enough.

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