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Foundation Subscription > Data Science > Supervised Learning: Key Steps
Course Description
In this course, you will explore the main steps for working on a supervised machine learning problem. First, you will learn the different sets in which data needs to be split for training, validating, and testing your model. Next, you will focus on the most common evaluation metrics. Finally, you will perform error analysis, with the purpose of understanding what measures to take to improve the results of a model.
Requirements
  • There are no pre-requisites for this course.
Instructor:
Hyatt Saleh; Samik Sen
Hyatt Saleh discovered the importance of data analysis for understanding and solving real-life problems after graduating from college as a business administrator. Since then, as a self-taught person, she not only works as a machine learning freelancer for many companies globally, but has also founded an artificial intelligence company that aims to optimize everyday processes. Samik Sen is currently working with R on Machine Learning. He has done his Ph.D. in Theoretical Physics. He has Tutored Classes for High-Performance Computing postgraduates and Lecturer at International Conferences. He has experience of using Perl on data, producing plots with gnuplot for visualization and latex to produce reports. He, then, moved to finance/football and online education with videos.