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

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

Artificial Intelligence

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3 courses

39 practices

8 hours

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.

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Certificate of Achievement

Verified skills you'll gain

INTERMEDIATE

Deep Learning and Neural Networks

ADVANCED

Machine Learning Model Development

Tools you'll use

C++

Course 1

Ensemble Methods

4 lessons

14 practices

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.

Course 2

Unsupervised Learning and Clustering

4 lessons

15 practices

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.

Course 3

Neural Networks

3 lessons

10 practices

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.

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