Welcome to the world of classification, one of the main types of modelling families in supervised Machine Learning! Through a series of engaging labs, you will delve into the entire classification process, starting from preprocessing your data to training and evaluating models. Additionally, you will learn how to effectively visualize and interpret the results and to handle data sets with unbalanced classes.
 
Classification serves as a critical foundation in data analysis, from categorizing data into their respective classes to training and fine-tuning generative LLMs that can generate new and meaningful content. In this course, different types of classification methods will be covered, showcasing which one is most suitable for a particular use case. 

By the end of this course, you should be able to:

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This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques in a business setting.

To get the most out of this course, you should have familiarity with programming in a Python development environment, as well as a fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.