Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. It is pervasive today in everyday life from recommendation engines to practical speech recognition, web searches to advanced GPS systems. Businesses are taking advantage of machine learning in creating advanced solutions to serve their customer segments.
Just as humans learn by example, machine learning algorithms learn by example. Machine learning allows us to both learn from the past to inform the future and give our data a voice. There are two equally important components for the successful application of machine learning: a good algorithm, and a comprehensive set of training examples that span as much of the system-of-interest parameter space as possible.
In this course, students learn about machine learning and the data preparation workflow. The course begins with a portfolio of case studies to provide an overview of what can be accomplished with machine learning. Then, the fundamental machine learning tasks and algorithms are covered. The machine learning tasks and algorithms covered include multivariate nonlinear non-parametric regression, supervised classification, unsupervised classification, and deep learning. For these machine learning tasks, it is shown how to assess the quality of the machine learning models and perform error estimation and feature engineering.
Module 0. About the Course (3 min)
Module 1. Introduction to Machine Learning (19 min)
Module 2. Introduction to Statistical Learning Theory (22 min)
Module 3. Supervised Learning (33 min)
Module 4. Unsupervised Learning (34 min)
Module 5. Deep Learning (37 min)
Module 6. Business Applications (24 min)
Click - here - to download a more detailed outline of this course.
This exam tests knowledge and understanding of basic concepts, principles, and terminology of data science.
Number of Questions: 23
Time Limit: 46 Minutes
Passing Score: 70%
Once you pass the exam, you will receive a Certificate of Education documenting that you have demonstrated mastery of the topic.
Further, the exam will count towards the
Certified Information Management Professional (CIMP) designation in the Data Science track.
We recommend that you take detailed notes and review the course material multiple times before taking this exam.
Click here to learn more about CIMP exams.