FULL BEGINNERS - Start here if you have 0 knowledge on Python/data science, and if you haven't done a lot of math
(mostly algebra & statistics) recently
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Data Quest- Great primer on Python, also basic data science and data visualization.
https://www.dataquest.io/

Kaggle: Machine Learning Tutorial: Great place to start 'top-down' machine learning. You get straight into the action by working on datasets the tutorial provides, while understanding the ML working pipeline.
https://www.kaggle.com/learn/machine-learning

INTERMEDIATE - Once done w/ beginners OR want to understand machine learning really well, look at these (prepare yourself for some more hands-on math. Specifically linear algebra)
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Machine Learning by Andrew Ng - The most...

FULL BEGINNERS - Start here if you have 0 knowledge on Python/data science, and if you haven't done a lot of math
(mostly algebra & statistics) recently
____________________
Data Quest- Great primer on Python, also basic data science and data visualization.
https://www.dataquest.io/

Kaggle: Machine Learning Tutorial: Great place to start 'top-down' machine learning. You get straight into the action by working on datasets the tutorial provides, while understanding the ML working pipeline.
https://www.kaggle.com/learn/machine-learning

INTERMEDIATE - Once done w/ beginners OR want to understand machine learning really well, look at these (prepare yourself for some more hands-on math. Specifically linear algebra)
_____________________
Machine Learning by Andrew Ng - The most popular/ highly acclaimed course for machine learning. Accessible to anyone, Professor Ng even does a "Linear Algebra review" to get you up to speed for what you need to work with ML. Know there is calculus involved, but this is glossed over as it is not critical to understand for implementation purposes
https://www.coursera.org/learn/machine-learning

Deep Learning by Andrew Ng - I HIGHLY recommend this course. Similar to the popular ML course, but this is a specialization of several courses focused on neural networks. You DO NOT have to had completed the Machine Learning course to do this one, so if you are interested in deep learning, you can dive right in. Be prepared for theory, and first implementing neural networks from scratch (this is where you should be comfortable with Python).
https://www.deeplearning.ai/courses/

Fast.ai's Deep Learning for Coders - Great resource to get started with deep learning very quickly, without needing to understand the underlying mechanics. While meant as a "beginner's course", I think you can appreciate what is happening more after taking Andrew's Deep Learning courses. You will definitely have a better time with this course after taking the one above.
https://course.fast.ai/

Deep Learning, Book by Ian Goodfellow - Read this book once you have taken Andrew's Deep Learning course (and/or the Fast.ai course). Goodfellow is the creator of GANs, a class of neural networks, and a fantastic instructor. Co-Authored by other leaders in the field such as Yoshua Bengio. This is THE BOOK to read if you want to become highly proficient in deep learning. Link to Deep Learning Book: http://www.deeplearningbook.org/

Best of luck!

Note* Most of the resources focus on/eventually lead to deep learning. Why? Because DL has proven to be most effective for a majority of "AI problem domains", by far compared to other popular ML and AI practices. Its not so much that you will become a specialist in only deep learning, but that you will have it as a tool to approach almost any problem you'd like in the field of AI.