This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

This course provides the basic fundamental knowledge about machine learning approach and different machine learning algorithms.

This course provides the basic to intermediate knowledge of software testing. It also shows some real application of testing with some valuable guidelines.

This course helps to clear basic concepts of git and how to use it. This course also covers basic to advance commands to maintain version controls for projects.

This course helps to clear the fundamental concepts of cybersecurity. It also gives a detailed view of cybersecurity and vulnerability and the methods to fix them.

This course helps to clear the concepts behind the fundamental data structure and algorithms. It’s a beneficial course for beginners to get a headstart in algorithms and data structures.