ML basic: Encouragement words

Hi, we are starting a nice journey in the Machine Learning world. I like an expression attributed to the Chinese sage Laozi (or Lao Tzu) saying "A journey of a thousand miles begins with a single step". My credo is to do things right. Another expression says if you want to do something faster, do it slower. The main focus of this site is to provide a general Fundamental Education in Reinforcement Learning. There's no way to do this without a good understanding of how some Machine Learning stuff works. RL is part of ML, and they work in parallel, especially modern Deep Reinforcement Learning. So, it is enormously important to get the right information in the right sequential order to move forward.

In this course, we will be focusing on pure Python implementations of ML algorithms and other ML tricks. We will also use some general Python packages such as NumPy for linear algebra and Matplotlib for visualization. Later on, you will use built-in ML Python libraries, but a real ML engineer should know and understand how it works if You want to do complicated stuff and/or do research in the future. This module will contain basic information about ML and other useful ML practices, which must become a strong foundation for future ML/RL engineers.

It's better to implement these algorithms and ideas on your own, especially by memory, and revisit them from time to time. It will take more time but will give you a higher long-term reward in the future, to speak in RL terminology. It will not be fast, but if to keep learning on this as daily basics and routine you will see the results.

So, it's time now to take the First Step in this breathtaking ML journey and will see where it bring you.