Skills Validated

• Core concepts of supervised and unsupervised learning.
• Implementation of linear and logistic regression models.
• Understanding of Support Vector Machines (SVMs) and kernel methods.
• Application of clustering and dimensionality reduction algorithms (K-Means, PCA).
• Best practices for model evaluation, including bias-variance trade-off.