Start Date: October 18, 2021
End Date: December 6, 2021
Machine Learning is one of six non-credit courses in the Certification in Practice of Data Analytics (CPDA) program. This course can be taken individually or as one of four courses required to receive the CPDA certificate of completion.
Machine Learning can be taken after Statistics and Data Mining in the CPDA program. After learning how to analyze data statistically and the data mining methodology, students now explore the study and application of various machine learning algorithms that can learn from and make predictions on data to drive towards actionable insights. These insights will be discussed and how they can be used to achieve a deeper understanding and better decision making.
The course is taught by faculty from the College of Engineering at The Ohio State University. The course is delivered in 100% distance learning format and includes instructional material equivalent a one semester credit hour class.
In this course students will learn fundamental topics such as nonlinear optimization, dynamic optimization, Markov decision problems, training of deep neural networks, clustering, classification, regression trees, and dimensionality reduction techniques for high dimensional data. Students work through various case studies so they can implement their new knowledge on meaningful industry problems and develop an understanding of how different industries are using Artificial Intelligence & Machine Learning (AI/ML). The goal of the course is to prepare the student to understand the application of AI/ML solutions in solving complex data problems in multiple domains and access various industries use of this new and exciting technological domain.
4 CEUs are granted upon successful completion of the course.
You Will Learn to:
- The history of AI/ML and the future possibilities.
- What are the common applications.
- How to formulate basic data-driven machine learning problems.
- Collect and analyze time-series data to build data-driven automation strategies
- Differentiate between supervised and unsupervised learning tasks.
- The application of various coding languages to implement supervised and unsupervised machine learning algorithms.
- Evaluate which learning algorithms are useful for what kind of tasks and use that knowledge in various case studies.
- Assess the limits of machine learning.
- Discuss the ethical implications of using machine learning in our daily lives.
This course can be taken individually, or as one of four courses required to receive the CPDA certificate of completion. However, it is required that participants will have taken Introductory Statistics for Data Analytics and Data Mining before this course. Students should have familiarity with basic probability and statistics (random variables, expectation, mean and covariance, characteristic functions, central limit theorem, etc.) and data science best practices (data mining methodology, data preparation, transformation, etc.).
This class requires you to use the statistical software package called R (The R Project for Statistical Computing;(http://www.r-project.org/). This software package is available as Free Software.
Students must complete this free training before starting the Machine Learning class.
- Hands-on tutorials are available in the Swirl system, which you can learn about at http://swirlstats.com/. In particular, “R Programming: The basics of programming in R” is an appropriate first tutorial for students who have never used R.
- An easier to use interface to R is available in the software package RStudio. This package is available for Windows, Mac, and Linux and can be downloaded for free from http://rstudio.org.
- From the CRAN archive at https://cran.r-project.org, you can download R for Windows, Mac, and Linux.
- An in-depth "Introduction to R" training manual is available at http://cran.r-project.org/doc/manuals/R-intro.pdf
Click Here to learn more about how this course is delivered 100% online!
Expected Time Commitment to Complete this Course
Each course is equivalent to a one semester credit hour class. Therefore each class consists of approximately 40 hours of class time that includes 12-13 hours of recorded faculty lectures and 23-24 hours of additional course work. Each course is seven weeks in length, so each week there is 5.7 hours of combined class time (40 hrs / 7 weeks). The average student should allow a 2:1 study-to-class-time ratio to complete the course. This means you should plan to study two hours for each one hour of class time. This equates to 11-12 hours each week to complete all course work. (5.7 hrs X 2 = 11-12 hrs). Based on a person's own personal strengths and experience, you should increase or decrease the ratio.
Cancellations and Refunds
A full refund minus a $75 administrative fee will be made if cancellation is received three weeks prior to the start of the course. No refunds within three weeks of the course start date.
Course Offering Dates
Each course offering in this program is faculty lead and therefore operates with a specific start date and end date. Students must complete each course during the specific time frame. Access to the online course and materials is removed when the course ends.