Description

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Paul van Loon – Classification – Fundamentals & Practical Applications – CFI Education

Classification – Fundamentals & Practical Applications

• Level 4
• Approx 2.5h to complete
• 100% online and self-paced

This course provides a comprehensive overview of classification problems, solutions, and interpretations along with walkthroughs of real-world scenarios.

Overview

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

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.

What you’ll learn

Classification Introduction

• Course Introduction
• Learning Objectives
• Downloadable Files

Classification Overview

• What is Classification?
• The Machine Learning Ecosystem
• Types of Classification – Binary
• Types of Classification – Multi-class
• Types of Classification – Multi-label
• Common Classification Use Cases
• Visualizing Classification
• Classification Algorithms

Logistic Regression Basics

• Logistic Regression Basics
• Visualizing Logistic Regression
• Logistic Regression Assumptions
• Probability, Odds and Log Odds
• Interpreting Log Odds and Coefficients
• Interpretation Scenario
• Logistic Regression in Excel
• Python – Logistic Regression 1
• Python – Logistic Regression 2

Classification Algorithms

• Algorithms Overview
• Naïve Bayes
• Naïve Bayes – Example
• K-Nearest Neighbors
• K-Nearest Neighbors – Example
• Support Vector Machines
• Decision Trees
• Decision Trees – Example
• Random Forests
• Python – Import & Explore Data
• Predictive Modeling Part 1
• Predictive Modeling Part 2

Classification Model Evaluation

• Model Evaluation Basics
• Confusion Matrix
• Evaluation Metrics
• Evaluation Example
• Precision Vs Recall
• Balancing Precision and Recall with F-score
• Is Accuracy the Best Choice
• The ROC Curve & AUC
• Underfitting and Overfitting
• Python – ROC Curve
• Python – ROC Interpretation
• Interpretability
• Interpretability Vs Explainability
• Feature Importance
• Partial Dependence Plots
• SHAP Values for Individual Observations
• Setting Up Evaluation Loops
• Python – Evaluation Metrics
• Python – Confusion Matrix

Conclusion

• Course Conclusion

Qualified Assessment

• Qualified Assessment

This Course is Part of the Following Programs
Why stop here? Expand your skills and show your expertise with the professional certifications, specializations, and CPE credits you’re already on your way to earning.

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