Help meet the expanding needs of business and industry by learning how to effectively utilize both Machine and Deep Learning techniques to add value to any business. Organizations today use these methods not only to improve their core business operations but also for developing new business models.
These methods are being applied in a diverse range of industries including sales, marketing, advertising, health care, criminal justice, finance customer support and cool new industries like self-driving cars and highly efficient automated homes.
- Learn how to apply the art and science of machine and deep learning to deliver new insights and improve the competitiveness of your business.
- Explain what kinds of problems are best suited for machine learning and deep learning.
- Understand and apply machine and deep learning software tools used to solve business problems.
- Explain a variety of learning algorithms and how they are applied to understand differences between unsupervised, semi-supervised, supervised and reinforcement processes.
- Learn methodologies and tools to apply algorithms using a wide range of real data types including structured and unstructured text, video, and images from internal or external sources (e.g. scraped web data) and evaluate their performance.
- Determine related software toolkits to consider and how to integrate them into existing data workflows.
- Utilize basic building blocks, general principles and cloud technologies such as Amazon Web Services (AWS) to design machine learning algorithms.
- Learn the tools and techniques of Natural Language Processing (NLP) and its use in the analysis of human-generated content.
- Understand common pitfalls and challenges using neural networks and deep learning tools.
- Understand what hardware or virtual machines are needed for deep learning
- Explain the difference between machine and deep learning versus traditional statistical data analysis techniques
Who Should Enroll
- Professionals in various industries and job functions who are looking to help their organisation leverage the massive amounts of diverse data and develop self-improving systems.
- Specific job titles that would benefit from this program include Marketing, Sales, Business Analysts, Data Engineers, Data Analysts, Computer Scientists, Database Administrators, Researchers, and Statisticians.
- Tools and Techniques for Machine Learning
- Artificial Neural Networks
- Text Mining and Analytics
Elective Courses (Minimum 4 units)
- Introduction to Big Data
- Introduction to Data Science
- Introduction to Predictive Analytics
- R Programming
- Introduction to Programming with Python
- Math Review for Data Science and Analytics
Specialized Studies Award Requirements
The Specialized Studies award is provided upon completion of 10 credit units (3 required courses and a minimum of 4 elective credit units) with a grade of “C” or higher in each course. All requirements must be completed within 5 years after the student enrolls in his/her first course. Students not pursuing a certificate are welcome to take individual courses.
English Proficiency Requirement
All certificate programs at UCI Division of Continuing Education (classroom and online formats) require professional-level English language proficiency in listening and note-taking, reading comprehension and vocabulary, written expression, and oral presentation.
This school offers programs in:
Last updated March 6, 2018