Implementing AI Algorithms from Scratch

Go beneath the surface of classification algorithms and metrics, implementing them from scratch for deeper understanding. Bypass commonly-used libraries such as scikit-learn to construct Logistic Regression, k-Nearest Neighbors, Naive Bayes Classifier, and Decision Trees from ground up. This course includes creating the AUCROC metric for Logistic Regression, among others.

Delve into the intricacies of optimization techniques with this immersive course that focuses on the implementation of various algorithms from scratch. Bypass high-level libraries to explore Stochastic Gradient Descent, Mini-Batch Gradient Descent, and advanced optimization methods such as Momentum, RMSProp, and Adam.

Learn about Ensemble Methods and their implementation from scratch. This course covers the understanding and implementation of multiple ensemble methods such as Bagging, Random Forest, AdaBoost, and Gradient Boosting Machines like XGBoost without relying on high-level libraries.

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.