ML courses#
Learning machine learning does not have to be very expensive or time consuming. Great learning material for machine learning is licensed under a Creative Commons license. For starters but also people who are already more familiar with the key concepts.
This section presents an opinionated list of great machine learning learning resources. A lot of garbage is produced on the internet and even paid courses are often not that good.
This list consist of very readable references and some great hands-on courses. Only resources that are real open, so resources published using a Creative Commons license (cc-by mostly) or other types of real open licensed material is included.
Most learning resources include hands-on tutorials. So be ready to learn how to use a notebook, but most tutorials offer notebooks ready to use.
Everyone in the world should have access to high-quality machine learning resources. This to empower Free and Open Machine Learning.
This list of open (Creative Commons licensed ) machine learning training resources contains resources for starters who never want to do ‘hands-on’. Openness for knowledge sharing means no user registration to read or play with the material is required.
Tip
Never stop learning!
Learn PyTorch for Deep Learning: Zero to Mastery book
Online book version of the Learn PyTorch for Deep Learning: Zero to Mastery course. With also a A Quick PyTorch 2.0 Tutorial
DEEP LEARNING(with PyTorch)
Supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.
Course from NYU CENTER FOR DATA SCIENCE, advanced course
TensorFlow, Keras and deep learning, without a PhD
Great hands-on course from google. Learn how to build and train a neural network that recognises handwritten digits. Along the way, as you enhance your neural network to achieve 99% accuracy, you will also discover the tools of the trade that deep learning professionals use to train their models efficiently.