This lecture covers the basic concepts of clustering, a machine learning technique used to group similar data points together based on their features. We will be focusing on KMeans algorithm implementation, evaluation techniques both supervised (using classification metrics) and unsupervised (using silhouette score, pairwise distance matrix, etc.), DBScan and AgglomerativeClustering algorithms as well as feature reduction using Principal Component Analysis (PCA) and T-SNE algorithms.

We'll demonstrate these concepts with python notebook utilizing the scikit-learn and scipy libraries to showcase how these techniques can be implemented in real world machine learning problems.

The .ipynb notebook it's ready for start, you just need to run it using your virtualenv or just upload it on Google Colab.

In addition, you can find the .devcontainer folder to use the Docker container as a Dev. Environment as shown during the lecture.