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.
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:link: https://d2l.ai/
{octicon}book;1em;caption-text Dive into Deep Learning
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Interactive deep learning book with code, math, and discussions. Implemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow.
Adopted at 400 universities from 60 countries
+++ Check this Course Book » :::
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:link: https://christophm.github.io/interpretable-ml-book/
{octicon}book;1em;caption-text Interpretable Machine Learning
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A Guide for Making Black Box Models Explainable
+++ Check this Course Book » :::
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:link: https://www.tomasbeuzen.com/deep-learning-with-pytorch/README.html
{octicon}book;1em;caption-text Deep Learning with PyTorch
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Optimization algorithms, neural networks for regression and classification tasks, convolutional neural networks for image classification, transfer learning, and even generative adversarial networks (GANs)
+++ Check this Course Book » :::
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{octicon}book;1em;caption-text Learn PyTorch for Deep Learning: Zero to Mastery book
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Online book version of the Learn PyTorch for Deep Learning: Zero to Mastery course. With also a A Quick PyTorch 2.0 Tutorial
+++ Check this book » :::
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:link: https://course.fast.ai/
{octicon}book;1em;caption-text Practical Deep Learning
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A course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.
+++ Check this course » :::
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:link: https://mlc.ai/index.html
{octicon}book;1em;caption-text Machine Learning Compilation
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Learn the key abstractions to represent machine learning programs, automatic optimization techniques, and approaches to optimize dependency, memory, and performance in end-to-end machine learning deployment.
Advanced course +++ Check this course » :::
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:link: https://bronwojtek.github.io/neuralnets-in-raw-python/docs/index.html
{octicon}book;1em;caption-text Explaining neural networks in raw Python:lectures in Jupiter
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The goal of this course is to teach some basics of the omnipresent neural networks with Python.
The text is brief so you can complete the course in a few afternoons!
+++ Check this course » :::
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:link: https://bait509-ubc.github.io/BAIT509/intro.html
{octicon}book;1em;caption-text BAIT509 - Business Applications of Machine Learning
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Introduction to machine learning concepts, such as model training, model testing, generalization error and overfitting.
Course given at University of British Columbia
+++ Check this course » :::
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{octicon}book;1em;caption-text DEEP LEARNING(with PyTorch)
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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
+++ Repository (with notebooks) :::
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:link: https://dafriedman97.github.io/mlbook/content/introduction.html
{octicon}book;1em;caption-text Machine Learning from Scratch
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This book covers the building blocks of the most common methods in machine learning. This set of methods is like a toolbox for machine learning engineers.
+++ Check this course » :::
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:link: https://fairmlbook.org/index.html
{octicon}book;1em;caption-text Fairness and machine learning, Limitations and Opportunities
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If machine learning is our way into studying institutional decision making, fairness is the moral lens through which we examine those decisions.
+++ Read this course book » :::
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:link: https://bloomberg.github.io/foml/#home
{octicon}book;1em;caption-text Foundations of Machine Learning
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Understand the Concepts, Techniques and Mathematical Frameworks Used by Experts in Machine Learning +++ Read this course book » :::
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:link: https://inria.github.io/scikit-learn-mooc/index.html
{octicon}book;1em;caption-text scikit-learn course
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The goal of this course is to teach machine learning with scikit-learn to beginners, even without a strong technical background. +++ Check this course » :::
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:link: https://developers.google.com/machine-learning/crash-course/
{octicon}book;1em;caption-text Machine Learning Crash Course with TensorFlow APIs
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Google crash-course (cc-by). A great course published by Google's. It is advertised as a 'A self-study guide for aspiring machine learning practitioners' +++ Check this course » :::
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:link: https://spinningup.openai.com/en/latest/index.html
{octicon}book;1em;caption-text Spinning Up in Deep RL
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Spinning Up in Deep RL, become a skilled practitioner in deep reinforcement learning. An educational resource produced by OpenAI, the company behind ChatGPT.
+++ Check this course » :::
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:link: https://www.elementsofai.com/
{octicon}book;1em;caption-text The Elements of AI, learn the basics of AI
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Our goal is to demystify AI. The Elements of AI is a series of free online courses created by MinnaLearn and the University of Helsinki. +++ Check this course » :::
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:link: https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist/#0
{octicon}book;1em;caption-text TensorFlow, Keras and deep learning, without a PhD
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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. +++ Check this course » :::
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