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).
Offered By
Introduction to Machine Learning
Duke UniversityAbout this Course
Learner Career Outcomes
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Learner Career Outcomes
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Syllabus - What you will learn from this course
Simple Introduction to Machine Learning
Basics of Model Learning
Image Analysis with Convolutional Neural Networks
Recurrent Neural Networks for Natural Language Processing
Reviews
- 5 stars74.62%
- 4 stars20.71%
- 3 stars2.76%
- 2 stars0.65%
- 1 star1.24%
TOP REVIEWS FROM INTRODUCTION TO MACHINE LEARNING
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.
It's really an amazing field to learn new things and from institute is like Amazing to me I've learnt more ...it's not at all boring and we'll will be excited for future experience with you 💯
This is the best course for the Machine Learning! I liked all the instructors, especially, I loved Lawrence Carin Sir's lectures simplified way of teaching! Thank you Team.
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 !
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