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.
Classification - Fundamentals & Practical Applications
This course is part of Practical Data Science for Data Analysts Specialization
Instructor: CFI (Corporate Finance Institute)
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November 2024
1 assignment
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There are 7 modules in this course
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.
What's included
1 video1 reading
What's included
8 videos
What's included
9 videos1 reading
What's included
12 videos
What's included
19 videos
What's included
1 video
What's included
1 assignment
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