Applied Machine Learning
Start Date: March 9, 2022
End Date: April 27, 2022
Applied 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.
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.
You 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
Software Used in this Course
- Google Colab: https://colab.research.google.com/
- Python Libraries:
- PyTorch Lightning: https://www.pytorchlightning.ai/
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 an understanding of Python before taking this course. If you don't have experience with Python, it's required that students complete this free training before starting the Machine Learning class: https://www.kaggle.com/learn/python
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.