Applied Machine Learning
Start Date: October 17, 2023
End Date: December 5, 2023
Applied Machine Learning is one of five 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.
Applied 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.
This course builds on the CPDA Data Mining course. Students will learn practical approaches to constructing scalable data pipelines for machine learning applications. Using various case studies, students will work through data ingestion, data pre-processing, and training and evaluating machine learning models on a variety of data types including tabular data, imagery/video, and natural languages (e.g., social media, literature, etc.). The purpose of this course is to develop a functional understanding of how to use machine learning models 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.
Students Will Learn to:
- Be competent with using a Python integrated development environment (IDE) to write well-structured code
- Be competent with using Python libraries and toolkits to import/export and analyze data for machine learning
- Be familiar with bottlenecks and pain points in real world data pipelines
- Be familiar with current machine learning models for different data types
- Be familiar with evaluating and tuning the performance of a machine learning model
Below is a video of the instructor for this course explaining what students will learn and can expect from this course.
Python for Data Analysis - Data Wrangling with Pandas, NumPy, and IPython, William McKinney, O'Reilly Media, 2017. An electronic version is available for students at https://library.ohio-state.edu/record=e1002334~S7
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 if pursuing the entire certification. Students need 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.).
Students must have experience using Python before taking this course. Once a student is enrolled in the Introductory Statistics course, or the Machine Learning course, they will also be given access to a preparatory course on Python created by the instructors of our Data Mining and Applied Machine Learning courses. This Python training course is free and students can complete it at their own pace. It is highly recommended that every student complete the Python training course if they wish to be successful in the Applied Machine Learning course and if they do not have experience with Python.
Expected Time Commitment to Complete this Course
Each course is equivalent to a one semester credit hour class with each class consisting of approximately 40 hours of class time (12-13 hours of recorded faculty lectures and 23-24 hours of additional course work). Each course is seven weeks in length. 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. We recommend students 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.
The course is graded pass/fail. Students must receive an 80% or better on all graded items to pass and receive the certificate of completion for the course.