Financial Analysis - Build a ChatGPT Pairs Trading Bot - How to Code Yourself (Part 1)

The lecture emphasizes the importance of coding independently in machine learning. It highlights the necessity of practice to truly understand concepts, comparing it to learning a sport. The lecture explains the benefits of coding by oneself, such as understanding details and correcting assumptions. It introduces supervised learning, emphasizing that data is universal across different applications. The lecture concludes with guidance on implementing algorithms, focusing on the predict and fit functions, and encourages trial and error in coding.

Why is it said that all data is the same when performing linear regression?

What is the role of the fit function in a machine learning model?

How can you determine the number of iterations for your loop in a machine learning algorithm?

What should you do if your learning rate is too high?