Microsoft introduces a machine-learning lecture/tutorials
Microsoft launched a 12-week, 24-lesson curriculum for machine learning. They cover the standard topics like linear and logistic regression and clustering but also topics like Natural Language Processing and time series forecasting. The topics look interesting, though, personally, I am not sure about the format. Instead of videos, standard lecture slides or a book, the authors decided to host their lessons on GitHub. I am not sure if this is the ideal format, but let us know in the comments what you think!
How can you build simple recommender systems with Surprise? [Amol Mavuduru]
A prototype of a “Similar Shoe” (Sneaker) Recommender-System with Source Code [Josh Barua]
‘Lazy Predict’ Tutorial for AutoML with scikit-learn [Eryk Lewinson]
About The Author
Joeran Beel
I am the founder of Recommender-Systems.com and head of the Intelligent Systems Group (ISG) at the University of Siegen, Germany https://isg.beel.org. We conduct research in recommender-systems (RecSys), personalization and information retrieval (IR) as well as on automated machine learning (AutoML), meta-learning and algorithm selection. Domains we are particularly interested in include smart places, eHealth, manufacturing (industry 4.0), mobility, visual computing, and digital libraries. We founded or maintain, among others, LensKit-Auto, Darwin & Goliath, Mr. DLib, and Docear, each with thousand of users; we contributed to TensorFlow, JabRef and others; and we developed the first prototypes of automated recommender systems (AutoSurprise and Auto-CaseRec) and Federated Meta Learning (FMLearn Server and Client).