Machine Learning Algorithms with Python in Business Analytics

Overview

One of the most exciting aspects of business analytics is finding patterns in the data using machine learning algorithms. In this course you will gain a conceptual foundation for why machine learning algorithms are so important and how the resulting models from those algorithms are used to find actionable insight related to business problems. Some algorithms are used for predicting numeric outcomes, while others are used for predicting the classification of an outcome. Other algorithms are used for creating meaningful groups from a rich set of data. Upon completion of this course, you will be able to describe when each algorithm should be used. You will also be given the opportunity to use Python to run these algorithms and communicate the results.

Syllabus

Course Orientation and Module 1: Introduction to Machine Learning (ML) for Testing and Predicting Business Data

In this module, we will examine why exploratory data analysis alone may not yield actionable business insights, evaluate the key steps of the machine learning workflow, and apply preprocessing techniques using scikit-learn to prepare data for analysis with various algorithms.

Module 2: Regression Algorithms

In this module, we will learn how regression can be used for prediction, relationship exploration, and inference in business contexts by applying scikit-learn and scipy, and will evaluate the effectiveness of regression models across different applications.

Module 3: Classification Algorithms

In this module, we will deepen our understanding of how classification algorithms are applied in business by running and interpreting K-nearest neighbors and decision tree models.

Module 4: Clustering Algorithms

In this module, we will gain a deeper understanding of how clustering algorithms are used in business by running and interpreting K-means and DBSCAN models.