In this course you will be introduced to the classification problem and a number of the approaches used to solve the problem. Each approach is presented with the underlying intuition as well as the necessary mathematical underpinnings. We discuss the learning algorithms and illustrate the python tools available using examples. You will learn the relative merits and demerits of each approach. The focus of the course is on learning to find the right model for the problem at hand using the available tools and experimentation. Throughout the course, exercises are provided to reinforce ideas.
What am I going to get from this course?
Learn several classification models that are widely in use.
Gain the knowledge and skills to effectively apply existing classification algorithms and tools to solve real-world problems.
Evaluate multiple models and select the most appropriate for the task at hand.
Prerequisites and Target Audience
What will students need to know or do before starting this course?
Students will benefit from prior exposure to probability and statistics, basic algebra and calculus. Familiarity with the Python programming language is required. Students should be able to use Python 3.x and Python Notebooks.
Who should take this course? Who should not?
Industry professionals and college students who are interested in learning about the available algorithms and tools to address machine learning problems in general, and specifically, the classification problem.