If you are interested in learning about Machine Learning, as everyone should be, I've compiled a list of resources that I've found to be quite valuable throughout my journey to become a Machine Learning Engineer.
Note: This isn't supposed to be a complete list of resources for Machine Learning. You can find those on the web easily. This will only list the resources that I used and found valuable.
Throughout my life, I've found books to be the most valuable resource that one could have in order to better understand a topic. Online courses are a good resource to give a general introduction to wet your appetite but in order to satisfy your craving to learn more and in detail, books are the best things you could ask for.
This books gives a general introduction to the different algorithms being used in the Machine Learning. Instead of relying heavily on math to explain the concepts, it uses ideas and models from the real-world. You can download this book for free although there is a catch, this book is a work in progress so some parts of the book and the algorithms might not have been written as of yet. But still, I highly recommend this book.
This book follows a more hands-on approach with Python programs accompanying different Machine Learning algorithms to further solidify the concepts. I find this way of teaching more effective as one could easily test out different algorithms and experiment with them to see what works and how. I haven't completed this book as of yet but I'm planning to do so in the near future.
Though books provide an in-depth knowledge about a certain topic, they aren't as effective as Video lectures when starting out. Video lectures are good for starting out in a new field or subject as they give a broad overview of the field and they are a more interactive medium for teaching about new concepts.
The Machine Learning Nanodegree offered by Udacity is one of the best resource for learning Machine Learning. The video lectures are well-structured and easy to understand. And the most important thing is the Projects that one have to complete in order to graduate from the Nanodegree. These projects help a lot in understanding the concepts that one is learning about by implementing them in some real-world scenario and seeing how it works.
You can also use Free Courses instead of buying the whole Nanodegree program which might be a little expensive for some. The only drawback is that you won't have access to the Projects but you can watch the Courses begin taught in the Nanodegree, free of cost.
This course, taught by Andrew Ng, is one of the few courses that manages to explain some of the most technical and math-heavy concepts in Machine Learning in an intuitive and easy-to-understand manner. Instead of shying away from using scary mathematical equations, it explains them in such a way that one starts wondering how come he didn't get that before.
I will update this as I learn more resources, so keep an eye out for more goodies.