Although ML and TinyML are very recent subjects to me, I’m very much engaged in developing these skills.
I’m gathering some educational resources, and I found this book:
Could someone tell me if it is a good starting point? Or recommend me some other resources.
I’m an environmental engineer with introductory skills in general computer science, and basic knowledge in microcontrollers. I’m pretty ok with algebra, calculus, statistics, mathematical modeling, etc…
The book is a great starting point! It takes you down a hands-on journey, which is also what we do in the TinyML edX course series. In fact, Pete Warden is one of the instructors on Course 3!
Also, in course 4, Daniel (the second author) will be chiming in on MLOps, so you are spot-on and in good company, learning straight from the source.
I have been using " Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" by Aurelien Geron in between TinyML courses and I find its approach adds greater depth to some things that were rushed over in the course in order to get us to the tiny parts within the allotted time for the course. In particular the part which I have completed about data preparation is very useful and I expect to eventually make time for a complete work through. I was able to get a copy from my local library but will probably buy it soon.
It takes you down a hands-on journey, which is also what we do in the TinyML edX course series. In fact, Pete Warden is one of the instructors on Course 3!