Skills you'll gain

There are 7 modules in this course

From Logistic Regression to KNN and SVM models, you’ll learn how to implement techniques in Excel and Python and how to create loops to run models in parallel.  Since model evaluation is so important, we’ll dedicate a whole chapter to interpreting model outputs with evaluation metrics and the confusion matrix. With this, you’ll learn about false negatives, and false positives, and consider the impacts these may have on specific business scenarios. Finally, we’ll give you a brief insight into more advanced classification techniques such as feature importance, SHAP values, and PDP plots. Upon completing this course, you will be able to: • Distinguish between classic classification techniques including their implicit assumptions and practical use-cases • Perform simple logistic regression calculations in Excel & RegressIt • Create basic classification models in Python using statsmodels and sklearn modules • Evaluate and interpret the performance of classification model outputs and parameters Whether you’re an aspiring data scientist, studying analytics, or have a focus on business intelligence, this classification course will serve as your comprehensive introduction to this fascinating subject. You’ll learn all the key terminology to allow you to talk data science with your teams, benign implementing analysis, and understand how data science can help your business.

Classification Overview

Logistic Regression Basics

Classification Algorithms

Classification Model Evaluation

Course Conclusion

Qualified Assessment

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