Cracking Classification
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.
Lessons and practices
Lesson 1: Logistic Regression Basics
Diagnosing Diseases with Logistic Regression
Complete the Logistic Regression Model
Feature Scaling for Logistic Regression
Logistic Regression Wine Classification
Lesson 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
Lesson 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
Lesson 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
Lesson 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
Interested in this course? Learn and practice with Cosmo!
Practice is how you turn knowledge into actual skills.