You’re right! I’ll demonstrate how linear regression can still be used.
In the following section, we will extend 1-D linear regression to any-dimensional linear regression, or how to build a machine learning model that can learn from multiple inputs.
Given a patient’s age and weight, we will use multidimensional linear regression to predict their systolic blood pressure. Finally, we’ll go over some practical machine learning issues to keep in mind when analysing data, such as generalisation, overfitting, train-test splits, and so on. There are no additional materials required for this course. Everything required (Python and some Python libraries) is available for free.
This course is designed for programmers who want to improve their coding skills by learning about data science. This course may be useful if you have a technical or mathematical background and want to know how to apply your skills as a software engineer or “hacker.”
This course emphasises “how to build and understand,” rather than “how to use.” After reading some documentation, anyone can learn how to use an API in 15 minutes. It is not a matter of “remembering facts,” but of “seeing for yourself” through experimentation. It will teach you how to visualise what is going on inside the model. This course is for you if you want more than a cursory look at machine learning models.
“If you can’t put it into practise, you don’t understand it.”
“What I cannot create, I do not understand,” said the great physicist, Richard Feynman.
My courses are the ONLY ones where you will learn how to build machine learning algorithms from the ground up. Other courses will teach you how to enter your data into a library, but do you really need assistance with three lines of code?
After repeating the process with ten datasets, you realise you didn’t learn ten new things. You only learned one thing and then repeated the same three lines of code ten times…
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