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Master Machine Learning, the Engine Behind Artificial Intelligence
Gain the skills to understand, build, and apply machine learning models in real-world scenarios. Whether you’re a business professional, data enthusiast, or tech beginner, this flexible, noncredit program gives you the foundation to thrive in today’s AI-driven world with no prior coding or math background required. Build your knowledge of machine learning and the data science behind AI learning models.
Self-paced, flexible online machine learning course to level up your skills
This self-paced machine learning course allows you to progress through modules, video lectures, and machine learning exercises to learn and improve your skills whenever it fits your schedule. There are no fixed deadlines or class times for this machine learning course, so you can balance your studies with work, family, or other commitments while learning online.
Machine Learning course information
In this beginner-friendly course, learners discover how machines learn from data and power today’s Artificial Intelligence. They explore how Machine Learning (ML) works, experiment with simple models, and gain an understanding of the major types of ML like supervised, unsupervised, and reinforcement learning, through interactive examples. The course also introduces the ethical dimensions of intelligent systems, learning algorithms, emphasizing bias, fairness, and accountability. By the end of the course, learners develop a clear understanding of emerging career pathways in AI, ML, and data analytics, gaining the confidence to continue their growth in this high-demand field.
- Machine Learning runs March 2 – May 2, 2026.
- Students must register by March 9.
- The eight-week course is entirely online and self-paced. You can complete the course anytime within the eight weeks the course is available.
The tuition for this course is $795, with a $35 transaction fee. No textbooks are required.
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Machine Learning Literacy
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Data Understanding & Preparation
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Basic Python for Machine Learning
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Exploratory Data Analysis (EDA)
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Understanding Machine Learning Models
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Feature Awareness
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Model Evaluation Basics
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Use of Machine Learning Tools & Platforms
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Ethical & Responsible AI Awareness
You may request to receive a digital badge that will be embedded with the competencies learned. This badge can be added to your resume, LinkedIn page, portfolio, or even shared with your current or future employer.
Meet your Machine Learning course instructor
Dr. Ranjidha Rajan
Dr. Ranjidha Rajan will be the instructor for this Introduction to Machine Learning course. She holds a doctoral degree in Learning Analytics and brings an interdisciplinary background that bridges data science, education, and technology. At MSU Denver, she teaches courses on data visualization, programming languages, and learning algorithms. Her research focuses on AI in education, deep learning analytics, and data visualization, with a special interest in how a generative AI model can enhance teaching, research, and advance student learning.
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Request more informationQuestions?
For questions, contact Brandy Schooler at 303-615-1234 or [email protected].
Contact Us
Email usPhone: 303-615-1234
Office Location:
Jordan Student Success Building
3rd Floor – #330
Auraria Campus
Mailing Address:
MSU Denver
Innovative and Lifelong Learning
P.O. Box 173362
Campus Box 6
Denver, CO 80217-3362