Skip to main content
grasp.study
About
Explore
Pricing
Log in
Try it for free
Back to course
👋
Welcome to a Grasp lesson
Create a free account
to keep studying and create your own study path.
Python and ML proficiency
·
Module 11
Supervised Learning: Classification
1
Logistic Regression from Scratch
Implement logistic regression for binary classification from scratch
2
Multiclass Logistic Regression with scikit-learn
Train and evaluate multiclass logistic regression models using scikit-learn
Decision Tree Classifier: Training and Hyperparameters
Train a decision tree classifier and describe its key hyperparameters
4
Building and Evaluating Random Forests
Build and evaluate a Random Forest classifier
5
Gradient Boosting Classifier: Build and Evaluate
Build and evaluate a Gradient Boosting classifier
6
SVMs: Kernels, C, and Classification
Train and evaluate a Support Vector Machine (SVM) classifier, explaining the role of kernels and the C parameter
7
Understanding Confusion Matrices
Construct and interpret a confusion matrix for model evaluation
8
ROC Curves and AUC for Classifier Comparison
Plot and interpret ROC curves and AUC scores to compare classifiers
Previous module
Supervised Learning: Regression
Next module
Unsupervised Learning and Dimensionality Reduction