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

Overview

Master machine learning by implementing regression, classification, and optimization algorithms from scratch in C++. Build models like linear regression, k-NN, and decision trees, and learn key evaluation metrics—no high-level libraries required.

Syllabus

Course 1: Regression and Gradient Descent
Course 2: Classification Algorithms and Metrics
Course 3: Gradient Descent: Building Optimization Algorithms from Scratch

Courses

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3 hours
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Dig deep into regression and learn about the gradient descent algorithm. This course does not rely on high-level libraries like scikit-learn, but focuses on building these algorithms from scratch for a thorough understanding. Master the implementation of simple linear regression, multiple linear regression, and logistic regression powered by gradient descent.
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5 hours
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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.
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4 hours
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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.