If you know machine learning, then it is a breeze. If you don't, then you will.
This time, I just did it. Took it as a challenge and to learn it. This topic was being postponed since the start. I thought will learn, will learn sometime because it didn't sound much exciting to me (or say, I was worried about its complexity, without learning it).
Finally took an opportunity and got started. And believe me, it amazed me. Like what!? Time-series forecasting is amazing. And again, if you know machine learning algorithms, then it will be so helpful for you.
As always (if you're familiar with my past human-understandable courses) this course will help you out from scratch to understand the nuts and bolts of time series expecting you are familiar with python (numpy and pandas).
Kindly start from folder 0. Me Greeting Me and follow your way through the course ending with the folder 7. See ya. I have tried to keep the language reader-friendly with many (believe me, many) kiddy stuff inside trying to make it as digestible as possible (even the maths!).
Kind note: I have chosen the theme of this course as if I were writing a note to myself in the future. That might or might not interest you about the details, but you might want to place your story in there!
The base, the instructor, and the guru of my course is Mr. Lazyprogrammer. This was my first interaction with his course, but he is AMAZING. The beauty and expertise that he delivers are fascinating.
Many of the simplifications given in the course are coming from him, although with some modifications to match our mood and writing style. You can check out his course on Udemy.
So, whenever I mention "an author", I am referring to the legend Lazyprogrammer.
Disclaimer: I have made this course only as a part to educate myself and anyone who is reading this. Taking the original course is appreciated. This GitHub course covers most of the lecture material (with given credits) in a simplified manner. Hope the reader understands my intent 🙏
Learning means doing. Read it as a story, practice it as a real-life
Thank you
Aayush ∞ Shah