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)
Understand RNNs, LSTMs, sequence modeling, and temporal data.
Clustering & Dimensionality Reduction
Explore unsupervised learning, clustering algorithms, and PCA.
Model Evaluation & Metrics
Learn about performance metrics, confusion matrices, and model evaluation.
Advanced Topics & Applications
Explore transfer learning, NLP, reinforcement learning, and modern ML applications.