After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
Learn the Basics of Machine Learning
This course will introduce the key elements of machine learning to the business leaders. We will focus on the key insights and base practices how to structure business questions as modeling projects with the machine learning teams.
Dive into the Model Specifics
You will understand the different types of models, what kind of business questions they help answer, or what kind of opportunities they can uncover, also learn to identify situations where machine learning should NOT be applied, which is equally important. You will understand the difference between inference and prediction, predicting probability and amounts, and how using unsupervised learning can help build meaningful customer segmentation strategy.
Prerequisites
There are no prerequisites for this course
Curriculum
Course outline
1
Machine learning and data use cases
Machine learning is used in many different industries and fields. It can fundamentally improve the business if applied correctly. This chapter outlines machine learning use cases, job roles and how they fit in the data needs pyramid.
- Machine learning and data pyramid50 XP
- Terminology clarification50 XP
- Order data pyramid needs100 XP
- Match tasks in data pyramid100 XP
- Machine learning principles50 XP
- Modeling types50 XP
- Find supervised and unsupervised cases100 XP
- Job roles, tools and technologies50 XP
- Job role responsibilities50 XP
- Match data projects with job roles100 XP
- Team structure types100 XP
2
Machine learning types
This chapter overviews different machine learning types. We will look into differences between causal and prediction models, explore supervised and unsupervised learning, and finally understand the sub-types of supervised learning: classification and regression.
3
Business requirements and model design
This chapter reviews key steps in scoping out business requirements, identifying and sizing machine learning opportunities, assessing the model performance, and identifying any performance risks in the process.
4
Managing machine learning projects
This chapter will look into the best and worst practices of managing machine learning projects. We will identify most common machine learning mistakes, learn how to manage communication between the business and ML teams and finally address the challenges when deploying machine learning models to production.
R
Machine Learning for Business
Course
Complete
