Course Overview

 

Course Description

In this course, we study the fundamental concepts and techniques required for developing machine-learning models to solve business problems by analyzing massive amounts of data to find interesting patterns that can be used to assist decision making or provide predictions. Topics covered include regression, decision trees, clustering algorithms, naïve Bayes classification, evaluation metrics, model refinement, ensemble methods, neural networks and deep learning, dimensionality reduction, and association rule mining.  Students are expected to analyze real-world data in business using machine learning tools.

Goals of the course:

• Describing major machine learning algorithms
• Learning how to implement the methods in Python using machine learning and the related packages
• Implementing machine learning models to solve small projects working with large real-world datasets

Learning Outcomes 

• Understand common algorithms in Machine Learning and tools to implement those algorithms
• Understand model refinement and performance improvement methods in Machine Learning
• Apply analytic methods to choose an appropriate model and evaluate its effectiveness
• Implement end-to-end solutions to business problems using common machine learning algorithms
• Translate the results of the analytic model to an effective course of action in terms of realizing and measuring the value of the model
• Implement solutions for business problems using deep learning algorithms and improve their performance by dimensionality reduction and ensemble methods
• Communicate the results of an analysis and resulting model – including strengths and limitations of the solution – to technical and non-technical audiences