Partially, this text written by Copilot, so there can be repetitions.
Personally, I don’t have a lot of experience myself with recommender systems. But I have compiled a few resources that I think are useful for learning about recommender systems:
Last year’s Groningen Machine Learning Month has a great set of notebooks to get started with machine learning. The notebooks are available in the notebooks links. There are also lecture recording available on YouTube (see the recording links).
Kaggle is a great place to practice your machine learning skills. It has a lot of datasets and competitions to get you started. You can also find a lot of notebooks from other people to learn from.
Coursera Stanford Machine Learning from Andrew Ng is a great introductory course to machine learning. It covers the basics of supervised learning, unsupervised learning, and neural networks. And it goes through the math behind the algorithms in a very intuitive way.
Google Machine Learning Crash Course is a great introductory course to machine learning. It covers the basics of supervised learning, unsupervised learning, and neural networks. It also has a lot of practical examples in Python.
Introduction to Deep Learning (MIT) is a great introductory course to deep learning. It covers the basics of neural networks, convolutional neural networks, recurrent neural networks, and more. It also has a lot of practical examples in Python.
Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Müller and Sarah Guido. This book is a great introduction to machine learning. It covers the basics of supervised learning, unsupervised learning, and neural networks. It also has a lot of examples in Python.
An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. This book is a great introduction to machine learning. It covers the basics of supervised learning, unsupervised learning, and statistical learning. It also has a lot of examples in R and Python.
Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. This book is a great introduction to deep learning. It covers the basics of neural networks, convolutional neural networks, recurrent neural networks, and more. It also has a lot of examples in Python.
3Blue1Brown makes videos about math, science, and more. He has a great series on neural networks and linear algebra.
Steven Brunton dives deep into the mechanics of machine learning, control theory, and more. He explains practical usage, mathematics and intuition behind the algorithms.
Yannik Kilcher makes videos about machine learning research papers, programming, and issues of the AI community, and the broader impact of AI in society.