Understand what machine learning is, its types, and real-world applications.

Learn how models learn from labeled data to make predictions.

Model relationships between variables using straight-line fits.

Classify data points using the sigmoid function and probability estimates.

Find optimal hyperplanes that separate classes with maximum margin.

Build tree-based models that make decisions through recursive partitioning.

Combine multiple decision trees for robust ensemble predictions.

Sequentially improve weak learners to create strong predictive models.

Build multi-layer perceptrons that learn complex patterns from data.

Discover hidden patterns in unlabeled data.

Reduce dimensionality by projecting data onto principal components.

Extend PCA to non-linear manifolds using the kernel trick.

Partition data into K clusters based on centroid distances.

Automatically discover useful representations from raw data.