Data Science and Machine Learning (Theory and Projects) A to Z - Building Machine Learning Model from Scratch: Linear Re

The video tutorial covers the implementation of a linear regression model using a single feature in a Jupyter notebook. It begins with data generation and preparation, followed by matrix manipulation to fit a regression model. The tutorial demonstrates the use of the least squares method from the Numpy library to estimate parameters. It also explores handling noisy data and fitting models in such scenarios. The video concludes with a discussion on building classifiers and clustering algorithms from scratch, setting the stage for future lessons.

What are the expected outputs when using the least square function from Numpy?

How does noise in the data affect the parameters estimated by the model?

What steps would you take to implement a linear regression model from scratch?

What are the advantages of building a classifier from scratch compared to using built-in libraries?