Cluster Analysis with Python & Scikit-learn Machine Learning :

This course introduces clustering, a key technique in unsupervised learning, using the scikit-learn library. Students will explore various clustering algorithms, understand their use cases, and learn how to apply them to unlabeled datasets. The course covers both foundational concepts and practical implementation, focusing on the strengths and limitations of each method.

Key topics include (Clustering Methods, Practical Applications, and Advanced Concepts) :

• Overview of Clustering Methods: A comparative analysis of popular algorithms like K-Means, DBSCAN, Spectral Clustering, and Agglomerative Clustering. Students will learn to select appropriate methods based on dataset characteristics, such as geometry and density.

• Input Data Formats: Insights into handling standard data matrices and similarity matrices, enabling effective use of clustering techniques for diverse data types.

• Practical Applications: Hands-on exercises to implement clustering algorithms, fine-tune parameters, and interpret results. Techniques like K-Means++ initialization and MiniBatchKMeans will be explored for scalability.

• Advanced Concepts: Topics include cluster validation, dimensionality reduction (PCA), and addressing challenges like the curse of dimensionality.

By the end of this course, students will be equipped to perform clustering analysis, evaluate its outcomes, and apply these techniques in real-world scenarios across domains such as text analysis, image processing, and customer segmentation.

Who this course is for:

• Enthusiasts eager to explore the world of Data Science