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

Overview

Master core AI algorithms by implementing them from scratch in C++ using mlpack. Build hands-on expertise in ensemble methods, unsupervised learning, clustering, and neural networks—without relying on high-level libraries.

Syllabus

Course 1: Ensemble Methods
Course 2: Unsupervised Learning and Clustering
Course 3: Neural Networks

Courses

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3 hours
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Learn about Ensemble Methods and their implementation from scratch in C++ using mlpack. This course covers the understanding and implementation of multiple ensemble methods such as Bagging, Random Forest, AdaBoost, and Stacking. Focus on building these models and aggregating their results without relying on high-level machine learning libraries beyond mlpack.
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3 hours
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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.
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2 hours
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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.