Coursera MOOC / Non-credit 0

Machine Learning for Engineers: Algorithms and Applications

Name: Machine Learning for Engineers: Algorithms and Applications
Price: 49 USD
Availability: InStock

Northeastern University

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.

82/100

CourseAsk score

What the provider tells you: 31/45
Who stands behind it: 35/35
How complete the listing is: 16/20

Scores how much the provider publishes and who stands behind it — not how well it is taught.

What you'll learn

understanding of supervised and unsupervised learning techniques
ability to implement machine learning algorithms using Python
familiarity with applications in natural language processing and computer vision

Course objectives

to introduce practical machine learning algorithms
to explain the theoretical aspects of machine learning
to teach implementation skills using Python and PyTorch

Machine Learning #deep learning #python #machine learning #dimensionality reduction #unsupervised learning #computer vision #natural language processing #supervised learning #pytorch #clustering

$49.00

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