Classification - Fundamentals & Practical Applications

Classification – Fundamentals & Practical Applications

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.

Classification Fundamentals Learning Objectives

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

This Classification Course is a Core Course of CFI’s BIDA™ Program

The
Business Intelligence & Data Analyst (BIDA)™ program will take you on a
highly interactive and hands-on journey through the world of data science and
business intelligence.

From theory and interactive questions to applied Python
workbooks, data science models, and interactive dashboards in Tableau, our
instructors will walk you through countless real-world scenarios and exercises.

The program is ideal for students who want to learn how to
analyze data more effectively, save time with automation, or be a general
data pro at work. With a well-rounded knowledge of both BI and data science,
learners are well-equipped to take on roles in Business Intelligence, Data
Science, Data Analysis, Quantitative Analysis, and other data careers.

Who Should Take This Course?

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.