Classification problems are one of the most common scenarios we face in data science. This course will help you understand and apply common algorithms to make predictions and drive decision-making in business. Whether you’re an aspiring data scientist, studying analytics, or have a focus on business intelligence, this course will give you a comprehensive overview of classification problems, solutions, and interpretations.
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.
Overview
Syllabus
- Getting Started
- Classification problems are one of the most common scenarios we face in data science. This course will help us understand and apply common algorithms to make predictions and drive decision-making in business. From Logistic Regression to KNN and SVM models, we'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, we’ll learn about false negatives, and false positives, and consider the impacts these may have on specific business scenarios. Finally, we’ll have a brief insight into more advanced classification techniques such as feature importance, SHAP values, and PDP plots.
- Classification Overview
- Logistic Regression Basics
- Classification Algorithms
- Classification Model Evaluation
- Course Conclusion
- Qualified Assessment
Taught by
CFI (Corporate Finance Institute)
