Machine Learning Fundamentals
Test your knowledge of basic ML concepts, terminology, and principles.
Linear Models & Regression
Master linear regression, logistic regression, and related concepts.
Decision Trees & Ensemble Methods
Explore decision trees, random forests, boosting, and ensemble techniques.
Support Vector Machines (SVM)
Understand SVMs, kernels, and margin-based classification.
Neural Networks Basics
Learn the fundamentals of neural networks and deep learning.
Convolutional Neural Networks (CNN)
Master CNN architecture, convolution operations, and image processing.
Recurrent Neural Networks (RNN)
Learn about RNNs, LSTMs, GRUs, and sequence modeling.
Clustering & Dimensionality Reduction
Explore unsupervised learning techniques for grouping and compression.
Model Evaluation & Metrics
Master evaluation metrics for classification, regression, and beyond.
Practical ML & Best Practices
Real-world ML techniques, preprocessing, and deployment considerations.