Implementing AI Algorithms from Scratch in C++ (Part 1)

This advanced course path develops a practical understanding of machine learning by guiding you through C++ implementations of foundational algorithms without relying on high-level machine learning libraries.

You will build regression and classification models, including linear regression, logistic regression, k-nearest neighbors, Naive Bayes, and decision trees. You will also implement stochastic and mini-batch gradient descent alongside Momentum, RMSProp, and Adam optimization methods.

The path also covers evaluating model performance with classification metrics such as AUC-ROC. It is intended for learners who are comfortable programming in C++ and want to understand how machine learning algorithms work at an implementation level.

Syllabus

Implement simple and multiple linear regression in C++
Build logistic regression using gradient descent
Construct k-nearest neighbors, Naive Bayes, and decision tree classifiers
Implement stochastic and mini-batch gradient descent
Apply Momentum, RMSProp, and Adam optimization methods
Calculate classification metrics, including confusion matrix, precision, recall, and AUC-ROC
Evaluate and compare machine learning model performance