Implementing AI Algorithms from Scratch in C++ (Part 2)

Navigate through the intricacies of Unsupervised Learning and Clustering in this hands-on course. Skip the high-level libraries and build core aspects of unsupervised learning methods from scratch, including k-Means, mini-batch k-Means, Principal Component Analysis, and DBSCAN. Learn to assess cluster quality with crucial clustering metrics like homogeneity, completeness, and v-metric.

Dive deep into the theory and implementation of Neural Networks. This course will have you implementing tools at the heart of modern AI such as Perceptrons, activation functions, and the crucial components of multi-layer Neural Networks. All of this without the help of high-level libraries leaves you with a profound understanding of the underpinning mechanisms.