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About the Book

This weekend is about Linear models, linear models are the simplest parametric methods because many problems, even intrinsically non linear ones, can be easily solved with these

Models, we can try to identify the decision surface that separates the two classes. linear regression, is one of these linear models and a very simple approach for supervised learning.

Linear Regression is a useful tool for predicting a quantitative(interval, ratio) response(output). It is not a time to waste, it’s time to learn and understand linear regression or even compare it with modern and advanced statistical learning algorithms, linear regression is still useful learning algorithm, and it’s used as a base for a newer and powerful algorithms, also many fancy techniques can be seen as a generalization of linear regression. Hence it is very important to have a good understanding of linear regression before deep diving to complex learning methods.

In this weekend, we review the following:

• Some of the key ideas underlying linear regression.
• The least square approach that most commonly used to fit this model.

About the Author

Hisham El-Amir

@hisham_elamir

Hisham Elamir is a data scientist with expertise in machine learning, deep learning, and statistics. He currently lives and works in Cairo, Egypt. In his work projects, he faces challenges ranging from natural language processing (NLP), behavioral analysis, and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.