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
13%
13%
Skills you will gain
- Natural Language Processing
- Convolutional Neural Network
- Machine Learning
- Python Programming
- pytorch
Learner Career Outcomes
13%
13%
Offered by
Syllabus - What you will learn from this course
Simple Introduction to Machine Learning
Basics of Model Learning
Image Analysis with Convolutional Neural Networks
Introduction to Natural Language Processing
Reviews
- 5 stars74.76%
- 4 stars20.59%
- 3 stars2.73%
- 2 stars0.67%
- 1 star1.23%
TOP REVIEWS FROM INTRODUCTION TO MACHINE LEARNING
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 tutors' instruction are very clear and make it easy to understand for people who has little background. In addition, the examples are useful and applicable in this lesson!
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.
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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