Here is a list of resources available freely on the internet at your disposal related to Machine Learning, Deep Learning, the mathematics behind them, and other related items:
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Linear Algebra course from MIT Opencourseware - These are the video lecture by the famous Prof. Gilbert Strang.
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Probability and Statistics course from Khan Academy - one of the best courses that I have ever seen.
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Mathematics for Machine Learning book by Deisenroth, Faisal, and Ong - One of the best books on the maths specific to ML/DL. The eBook is available for free on their website, along with the codes from the book on their GitHub repository.
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Multivariate Calculus course by Khan Academy - again, one of the best courses related to this topic.
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Comprehensive list of Statistics resources on Kaggle.
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The Elements of Statistical Learning book by Hastie, Tibshirani ,and Friedman - the eBook is freely available on their website. However, I suggest buying the physical book because this book is an asset for every ML/DL practitioner.
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Comprehensive list of Calculus resources on Kaggle.
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Comprehensive list of Algebra and General Maths resources on Kaggle.
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Hands-On Machine Learning book by Geron - this book is one of the best books on ML/DL and was referred to by @sgiri in one of the ML/DL specialization videos. However, this is not freely available on the internet.
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Deep Learning book by Goodfellow - one of the classic texts on DL. A bit outdated but still an entertaining read. The eBook is available on the website for free.
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Machine Learning book by Mitchell - one of the classic texts on ML. A bit outdated but still relevant. The eBook is available on the website for free.
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Pattern Recognition and Machine Learning book by Bishop - another classic text on ML, also has a basic introduction to the maths of ML. The eBook is available for free on the website.
I will keep updating this list as and when possible. Feel free to contribute because remember, we can grow individually only when we grow together.