![]() 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.). 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. This course can be taken individually, or as one of four courses required to receive the CPDA certificate of completion. An electronic version is available for students at Python for Data Analysis - Data Wrangling with Pandas, NumPy, and IPython, William McKinney, O'Reilly Media, 2017. Be familiar with evaluating and tuning the performance of a machine learning modelīelow is a video of the instructor for this course explaining what students will learn and can expect from this course.Be familiar with current machine learning models for different data types.Be familiar with bottlenecks and pain points in real world data pipelines.Be competent with using Python libraries and toolkits to import/export and analyze data for machine learning.Be competent with using a Python integrated development environment (IDE) to write well-structured code. ![]() 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.Ĥ CEUs are granted upon successful completion of the course. 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). 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.). ![]() Students will learn practical approaches to constructing scalable data pipelines for machine learning applications. This course builds on the CPDA Data Mining course. The course is delivered in 100% distance learning format and includes instructional material equivalent a one semester credit hour class. The course is taught by faculty from the College of Engineering at The Ohio State University. These insights will be discussed and how they can be used to achieve a deeper understanding and better decision making. 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. This course can be taken individually or as one of four courses required to receive the CPDA certificate of completion.Īpplied Machine Learning can be taken after Statistics and Data Mining in the CPDA program. Applied Machine Learning is one of five non-credit courses in the Certification in Practice of Data Analytics (CPDA) program.
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