This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with PyTorch, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more).
Introduction to Machine LearningDuke University
About this Course
Lawrence CarinJames L. Meriam Professor of Electrical and Computer Engineering
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
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TOP REVIEWS FROM INTRODUCTION TO MACHINE LEARNING
I felt that I took the best descition in taking this course, because the professors took this course with atmost clarity and made even the difficult concepts understand easily. Thank you Professors
The course covers all the topic's regarding the machine learning and has an excellent explanation of concepts and the slides are very easy to understand thank you for such a wonderful course !
Thanks Coursera and Duke University for this course. It is very insightful to get understood the basics of ML and applied ML in numerous fields. It really made me to move ahead with ML domain.
The exercises are too much toy for people who already have certain knowledge of machine learning and are not feed forward for students that has no experience solving machine learning problems.
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