This course is aimed at anyone interested in applying machine learning techniques to scientific problems. In this course, we'll learn about the complete machine learning pipeline, from reading in, cleaning, and transforming data to running basic and advanced machine learning algorithms. We'll start with data preprocessing techniques, such as PCA and LDA. Then, we'll dive into the fundamental AI algorithms: SVMs and K-means clustering. Along the way, we'll build our mathematical and programming toolbox to prepare ourselves to work with more complicated models. Finally, we'll explored advanced methods such as random forests and neural networks. Throughout the way, we'll be using medical and astronomical datasets. In the final project, we'll apply our skills to compare different machine learning models in Python.
RJ
I would have had more stars, but a couple of the programming assignments had different values for random used for the answer and not what was listed in the question.
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I would have had more stars, but a couple of the programming assignments had different values for random used for the answer and not what was listed in the question.
Awesome course! Topics are discussed to easily grasp the ideas. Sorry, I'll put it there but the Introduction to Data Science and scikit-learn in python's autograder is not working. I could not get the certificate because of that
The course contains many external links, some of which are accessible only upon (paid) membership after a certain number of free views. Other links redirect to Wikipedia; less high-level reading and more hands-on exercises would be beneficial. Also, there were a few mistakes in the instructions of one of the programming assignments. Most of the content is accessible to beginners, but at times it seemed much harder. All in all, the course does provide a good general understanding of the main methods in ML.