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
A very concise and yet beautifully constructed course for introduction to machine learning for absolute beginner having basic knowledge of probability and mathematics.
Thank you so much to all the instructors there, for teaching the actual Machine Learning 😃😃 I have learned lot of things from this course and it will surely help in my future,😃🤝
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.
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.
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