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Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
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GitHub - kaleko/CourseraML: I took Andrew Ng's Machine Learning course on Coursera and did the homework assigments... but, on my own in python because I love jupyter notebooks! Use Git or checkout with SVN using the web URL.
Which is the best machine learning assignment for Coursera?
▸ Principal component analysis to find a low-dimensional representation of face images. ▸ Anomaly detection algorithm to detect failing servers on a network. ▸ Collaborative filtering to build a recommender system for movies. Click here to see solutions for all Machine Learning Coursera Assignments.
When to work on machine learning programming assignments?
I would usually work on the programming assignments on Sundays and spend several hours coding in Octave, telling myself that I would later replicate the exercises in Python. If you’ve taken this course and found some of the assignments hard to complete, I think it might not hurt to go check online on how a particular function was implemented.
Last Update: Oct 2021
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