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).
Ofrecido Por
Introduction to Machine Learning
Universidad DukeAcerca de este Curso
Resultados profesionales del estudiante
13%
13%
Resultados profesionales del estudiante
13%
13%
Ofrecido por
Programa - Qué aprenderás en este curso
Simple Introduction to Machine Learning
Basics of Model Learning
Image Analysis with Convolutional Neural Networks
Recurrent Neural Networks for Natural Language Processing
Reseñas
- 5 stars74,63 %
- 4 stars20,71 %
- 3 stars2,75 %
- 2 stars0,65 %
- 1 star1,24 %
Principales reseñas sobre 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
Thank you so much! This course is so amazing and more knowledge to know about how important or importance of machine learning.Thank you so much for this course you offer.
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.
Very good introductory course, I highly recommend it to anyone looking to get a flavour of the methods behind the recent advances in AI without going into super-technical details.
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