Data Science
645 learners
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.
Python
Scikit-learn
5 lessons
21 practices
3 hours
Machine Learning and Predictive Modeling
Lessons and practices
Diagnosing Diseases with Logistic Regression
Complete the Logistic Regression Model
Feature Scaling for Logistic Regression
Logistic Regression Wine Classification
Adjust Decision Tree Depth
Train and Predict with Decision Tree Classifier
Train the Decision Tree Classifier
Comparing Logistic Regression and Decision Tree Models
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
Detective Model Accuracy Calculation
Detective Work: Fix the Clue Classification
Train Naive Bayes Classifier
Comparison of Logistic Regression and Naive Bayes
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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