Enrolment options
This self-paced course will guide you on how to frame machine learning (ML) problems and then propose a solution. It does not cover how to implement ML or work with data
Who is this course for? This course is aimed at learners at the intermediate level who already have a basic understanding of ML but would like to understand how to frame ML problems
What will you learn? By the end of this course you will understand general methods of ML problem solving and be able to compare and contrast ML to other programming methods. You will be able to identify whether to solve a problem with ML as well as apply hypothesis testing and the scientific method to ML problems
Topics overview: ML mindset, predictions vs decisions, clustering, anomaly detection, success and failure metrics, ML model outputs
How much time you need to invest: This course should take approximately two (2) to three (3) hours to complete
Prerequisites: There are no prerequisites for entry but a basic understanding of ML will be beneficial
Course certificate: A course certificate will be generated based on the successful completion of the final quiz
Course developers: This course was developed by Google Developers. One must be aware that similar courses are offered by other vendors but for the purposes of this exercise, the course offered by Google Developers was selected
Course contact: fairforward@giz.de
License: CC BY SA 4.0