Machine Learning and AI for Engineers

This course introduced the basics of Machine Learning, Deep Learning(Pytorch and TensorFlow) and Reinforcement Learning.

Learning Outcomes:

1. Regression Models - Linear, logistic and Classification Models - Binary, Multinomial.
2. Feature Engineering.
3. Generative Models - Multivariate Gaussian Distribution, Gaussian Discriminant Analysis (GDA) and Naive Bayes.
4. SVD, PCA and SVM.
5. Clustering Algorithms - K Nearest Neighbors, K-Means and Gaussian Mixture Model.
6. Basics of Reinforcement Learning

Programming Language: Pytorch, Tensorflow, Python