This course is designed for learners who already have a foundational understanding of machine learning and want to elevate their skills by diving into advanced supervised and unsupervised learning methods. Through practical, end-to-end coding exercises and real-world datasets, you’ll master K-Nearest Neighbors (KNN), decision trees, and ensemble methods like random forests, AdaBoost, XGBoost, and CatBoost. Each lesson focuses on the intuition behind the models, proper data preprocessing, and techniques for tuning hyperparameters to improve performance.

Beyond supervised learning, the course delves deeply into clustering and unsupervised learning, offering comprehensive coverage of K-Means clustering, DBSCAN, spectral clustering, and hierarchical clustering. You’ll implement these models in Python using Scikit-learn, perform customer segmentation, visualize clusters in 2D and 3D, and understand how to apply the elbow method for optimal cluster selection. Whether you're tackling structured data, exploring complex relationships, or segmenting customers, this course provides the toolkit and strategies you need.

By the end of the course, you’ll be able to build predictive models, perform feature selection, apply cross-validation, and implement scalable machine learning pipelines. You’ll also be equipped with a solid understanding of ensemble techniques and clustering methods that are critical for real-world analytics and machine learning deployment.