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.
- A computer/Laptop with 64-bit processor
- A computer/Laptop with at least 4 GB RAM
- A computer/Laptop with 5 GB of storage needed to download and install the Anaconda Distribution of Python and R programming language.
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