Statistical Learning for Engineering Part 1
By Coursera
on Coursera
· Data Science
About This Course
This course covers practical algorithms and the theory for machine learning from a variety of perspectives. Topics include supervised learning (generative, discriminative learning, parametric, non-parametric learning, deep neural networks, support vector Machines), unsupervised learning (clustering, dimensionality reduction, kernel methods). The course will also discuss recent applications of machine learning, such as computer vision, data mining, natural language processing, speech recognition and robotics. Students will learn the implementation of selected machine learning algorithms via python and PyTorch.
Instructor
Qurat-ul-Ain Azim
Frequently Asked Questions
How much does Statistical Learning for Engineering Part 1 cost?
Visit the Statistical Learning for Engineering Part 1 course page for current pricing and available discounts.
Who teaches Statistical Learning for Engineering Part 1?
Statistical Learning for Engineering Part 1 is taught by Qurat-ul-Ain Azim, Northeastern University .
What skill level is Statistical Learning for Engineering Part 1 for?
This course is designed for all levels learners.