Introduction to Machine Learning
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).
3.5 rating, based on 2 Class Central reviews
Anonymous completed this course.The theory is ok and videos are well created. The practice is also fine, but not working properly. The page sends you to a Duke U site that is not working right now :-P). But even if it worked, since is a site outside of coursera, the activity never...
Excellent theoretical introduction, useful for those who know something but still do not have a broader picture.
There are also Jupiter notebooks where you can find the implementation in Python, but unfortunately it is additional material and is not explained in the videos.