Machine Learning: Fundamentals and Algorithms

Take a self-assessment test to verify your readiness for the math and Python content in this program.

The Machine Learning: Fundamentals and Algorithms from Carnegie Mellon University School of Computer Science Executive Education is a 10-week online program designed for technologists, data analysts, engineers, and data managers looking to advance their artificial intelligence (AI) and machine learning skills. With the technology paradigm shifting rapidly toward machine learning and AI, the proficiency of future-ready executives must also shift, expand, and advance. This online program provides you with the technical knowledge and analytical methods that will prepare you for the next generation of innovation.

The program requires a functional knowledge of high-school-level linear algebra, calculus, probability, statistics, and Python programming.

The program enables you to:

• Synthesize components of machine learning to create functional tools for the prediction of unseen data

• Implement and analyze learning algorithms for classification, regression, and clustering

• Use concepts from probability, statistics, linear algebra, calculus, and optimization to describe and refine the inner workings of machine learning algorithms

This program is especially ideal for:

Engineers in IT products and services, health care, or banking and financial services who want hands-on instruction in the tools and techniques of machine learning. Representative roles include:

• Software engineer

• Software developer

• Automation engineer

• Design engineer

Data analytics professionals in the banking, finance, and IT products and services industries, with responsibility for publishing reports, innovating, and working with analytics in a data-dense environment. This program will be especially relevant for analysts seeking to implement machine learning in projects or to upgrade from spreadsheet-based analysis to more powerful, programmatic data analysis. Representative roles include:

• Analyst

• Business analyst

• Data scientist

• Data analyst

Technical managers and directors of data functions leading a team of coders in banking and financial services, IT, health care, retail, logistics, or industrial goods who want to create enterprise value and gain hands-on skills in machine learning technology with the goal of solving business pain points. Representative roles include:

• Tech lead

• Senior engineer

• Senior developer

• VP of Engineering

• VP of Technology

• VP of Analytics

• Director of Business Systems & Information Technology

• Director of customer experience

• Data & integration director

• Technology director

Prerequisites: Prior experience with coding is required as participants will be expected to write their own code from scratch. Before enrolling, we strongly encourage you to complete the provided self-assessment exercises to evaluate your competency in mathematics content, Python programming language, and Jupyter notebooks. A passing score indicates your readiness for the rigorous program material, but does not guarantee success. Should you not pass these self-assessments, we recommend you strengthen gaps and weaknesses in your core knowledge and programming skills until you achieve proficiency before program participation.

At Carnegie Mellon’s Executive Education Program in the School of Computer Science, we provide organizations access to the skills and tools necessary to solve real-world technical problems by equipping the next generation of technology leaders with the experience, insights, and novel solutions developed by our community of computer science experts. From custom training programs to online individualized learning, our cutting-edge programming—backed by faculty who pioneered the field—takes your skill set to the next level, giving you the tools to tackle your organization’s next technological challenge.

Didn't find what you were looking for? Write to us at learner.success@emeritus.org or Schedule a call with one of our Program Advisors or call us at +1 315 756 3771 (US) / +44 203 835 5826 (UK) / +65 3138 2533 (SG)