Machine and Deep Learning

Machine Learning and Natural Language Processing define the current state of the art of Artificial Intelligence. These technologies, which are a form of data mining and data analysis, continuously learn from the provided information. They recognize hidden patterns that often provide dramatic competitive advantages, at relatively low costs to the organization. These technologies create significant improvements in the way we work, interact, and live – producing efficiencies never imagined before. These methods are being applied in a diverse range of industries, including sales, marketing, advertising, health care, criminal justice, finance customer support, cool new industries like self-driving cars, and highly efficient automated homes. Organizations today use these methods not only to improve their core business operations but also for developing new business models.

Deep Learning focuses on those Machine Learning tools that mimic human thought processes. Deep Learning can utilize a wide range of very large data sets (Big Data) in a vast array of formats (unstructured text, speech, images, audio and video). Machine Learning is dependent upon given features of the data to perform classification, detection, or prediction. Deep Learning is not dependent upon the representation of the data. It builds complex concepts from simpler models or data the same way the human brain processes large sets of inherently simpler stimuli to classify, recognize, analyze and synthesize. This layering of the network from simple to complex is Deep Learning. As these networks are inspired by the human brain, they are also called Artificial Neural Networks. Deep Learning algorithms and models are “trained” by the data (with guidance and monitoring from human insight) to solve a particular problem. Deep Learning Systems are designed to self-improve and get better as they process more data again mimicking how the human mind works.

Advances in Machine Learning and Deep Learning are helping to solve a very broad variety of problems including logistics, business process optimization, customer service, and health care. By 2020, IT departments will be monitoring 50 times more data than they are today. This tidal wave of data has driven unprecedented demand for those with the skills required to manage and leverage these very large and diverse data sets into a competitive advantage for their organizations.

According to a recent McKinsey report, one of the key barriers in the adoption of Machine Learning is attracting and retaining the right talent in businesspeople that combine data skills with industry and functional expertise. This program has been designed to help meet these expanding needs of business and industry for professionals who can effectively utilize both Machine and Deep Learning techniques to add value to any business.

Who Should Enroll

This program is intended for professionals in a variety industries and job functions who are looking to help their organization leverage the massive amounts of diverse data they collect and develop self-improving systems that improve their organization's ability to compete in the global marketplace. Specific job titles that would benefit from this program include: Marketing, Sales, Business Analysts, Data Engineers, Data Analysts, Computer Scientists, Database Administrators, Researchers, Statisticians, and those professionals looking to broaden their skills in this high-demand field while leveraging their unique domain expertise.

Program Benefits

• Learn from industry experts how to deliver new insights and improve the competitiveness of businesses by applying the art and science of machine and deep learning
• Explain what kinds of problems are best suited for machine learning and which are best suited for deep learning
• Understand and apply machine and deep learning software tools used in industry 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 which 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 which virtual machines are needed for deep learning
• Explain the difference between machine and deep learning versus traditional statistical data analysis techniques

Eligibility and Requirements

Apply to become a candidate for the program: A Declaration of Candidacy is required to establish candidacy in this program. Complete the application and pay the application and candidacy fee of $125 (non-refundable). Learn more about the benefits of becoming a candidate. Candidacy is not required to enroll into individual course in a program.

A candidate in the program is awarded a specialized studies certificate upon the successful completion of three (3) required courses and two and a half (2.5) elective units totaling 10 units, each with a letter grade of “C” or better. All requirements must be completed within five (5) years after the student enrolls in their first course. To receive the Certified Digital Certificate after the completing all the program requirements, students must submit the Request for Certificate to initiate the certificate audit process. Students not pursuing the certificate are welcome to take as many individual courses as they wish.