This was our first study group review session on Data Preprocessing and Linear Regression (Part 1 and 2 of the Udemy Machine Learning A-Z course).

Summary

Data Preprocessing

Data Preprocessing is the first step in almost any machine learning workflow. Proper data processing ensures that our machine learning algorithm are solving a numerically well-posed problem (in other words it makes learning easier). Slides

Linear Regression

Linear regression is probably the most ubiquitous technique in all of supervised learning. It forms the basis for many other techniques such as support vector machines and neural networks. Code

Presenters

Jim Hogarty

Jim is passionate about studying computer algorithms and their application to real-world problems. He is a lifelong entrepreneur with experience as founder and owner of a Honolulu systems integration firm, and a visual effects industry software company. Jim is presently a Professional Real Estate Agent with Locations, and enjoys using technology to facilitate and streamline business processes.

Ahnate Lim

Ahnate enjoys creative problem solving and collaborations using novel syntheses of ideas from a variety of fields. From the university lecture hall to the MRI neuroscience lab, from qualitative data analyses for Pacific islands cancer registries to quantitative multilevel analyses of state practitioners trainings, he has used and presented on a gamut of analytical methods. His current goals are to immerse myself within rich real-world data settings and develop pivotal products and applications.

Additonal Material

Additional material can be found on our github.