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Cracking Classification

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Overview

This course focuses on key classification techniques and evaluation metrics, including logistic regression, decision tree, and k-nearest neighbors (KNN) classifiers. You will understand how to compare and evaluate classifier performance using various metrics.

Syllabus

Unit 1: Logistic Regression Basics

Diagnosing Diseases with Logistic Regression
Complete the Logistic Regression Model
Feature Scaling for Logistic Regression
Logistic Regression Wine Classification

Unit 2: Decision Tree Classifier Basics

Adjust Decision Tree Depth
Train and Predict with Decision Tree Classifier
Train the Decision Tree Classifier
Comparing Logistic Regression and Decision Tree Models

Unit 3: K-Nearest Neighbors (KNN) Basics

KNN Flower Classification with Iris Dataset
Adjust K Value for KNN Classifier
Complete the KNN Classifier for Iris Dataset
Classify Iris Flowers with KNN
Flower Classification with KNN

Unit 4: Naive Bayes Basics

Detective Model Accuracy Calculation
Detective Work: Fix the Clue Classification
Train Naive Bayes Classifier
Comparison of Logistic Regression and Naive Bayes

Unit 5: Support Vector Machine (SVM) Basics

Changing SVM Kernel
Complete the Wine Classification SVM
Bringing Out the Power of the RBF Kernel
Tuning and Comparing Models Performances

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