Although this is introductory course we assume that you have some basic Python knowledge and you can figure out the environment settings yourself. We accept all graded assignments with a Python 3.8+ code in Jupyter Notebook.
You need to install Anaconda which means you are getting a version of Python 3.8 automatically and then follow the Jupyter Notebook quick guide to make sure that everything works correctly. If you are a more advanced user with Python already installed and prefer to manage your packages manually, you can just use pip. Answers on questions like "What is Jupyter Notebook?" or "How to use it?" can be found in this short intro guide (read first chapter up to "Example Analysis").
If you don't have enough computer power to complete the steps above or something just went wrong and you stuck, feel free to use Google Colab! It has mostly everything pre-installed and all you need for using it is your web browser. What is it and how to use it in a nutshell and explained in detail.
If you are already familiar with Python language you can test whether you knowledge is enough to dive into the course. Don't worry if you cannot complete all of the tasks provided, fully completing the first part (Fundamentals, Strings, Arrays) will be good enough for start.
If the Python is new language for you please refer to a start guide and then try to do the first few tasks. Later on when you need a deeper understanding of language concepts, please refer to this book chapters (1, 2, 3, 4, 5 and 7.1, 7.2, 7.3, 7.4).
You also should take a look at Numpy library which we are going to work closely with.
Don't worry, these articles require only basic understanding of school mathematics. They cover only the simplest and most necessary concepts needed for understanding the basic ML models we use in this course.
The situation is similar to that of Linear Algebra articles. Only simple and necessary concepts and definitions.