Introduction to Machine Learning with SciKit LearnCracking 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.

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