Machine & Deep Learning Program

computer network shapped as a brain 

Machine Learning and Deep LearningCertificate Program

Apply Now   Get More Information


An eleven-week certificate program covering machine learning and deep learning for IT professionals

DePaul University's Machine Learning and Deep Learning Program is designed for IT professionals who want to understand the fundamental principles of machine learning and deep learning and be able to apply them to their businesses. The program is suitable for data scientists and analytics professionals wanting to make the transition from business intelligence to modern data analytics and to bring their machine learning knowledge to the next level—deep learning. The program focuses on machine learning, transfer learning, automated machine learning, and deep learning. The program covers theoretical concepts as well as building of practical skills through several labs where the student will build his own models, and tune and deploy models. The program will prepare students with the necessary skills to create efficient machine learning and deep learning applications to solve business problems and improve business processes.

Program content consists of lectures and demonstrations complemented with hands-on labs. Reading assignments, case studies, group discussions, and projects will be assigned. In order to maximize learning, students will be required to bring their own laptop computer to every class session. Several cloud-based products for machine learning and deep learning will be explored. While access to most of these cloud services will be provided to students in class, there may be some cloud services that are only accessible via the use of the student’s own credit card. Students should expect to spend a small fee to access these services.

Students in this program will be able to:

  • Identify basic concepts, algorithms, and methods in the fields of machine learning and deep learning
  • Understand the capabilities provided by various big data analytics frameworks integrated with different machine learning and deep learning platforms offered by Google, Amazon, Microsoft, IBM, etc.
  • Clean and analyze data using programming languages, packages, and tools like Python, R, SQL, etc.
  • Create effective visualizations to maximize comprehension of complex data sets
  • Distill vast stores of complex, unstructured data into actionable insights to support data-driven initiatives
  • Gain insight into the use of machine learning and deep learning along with advanced analytics to identify customer behavior patterns and make the best use of available data
  • Recognize problems that can be solved with deep learning and select appropriate techniques for problem solving
  • Use various deep learning algorithms and tools to build models to solve business problems
  • Master deep learning for computer vision and object detection
  • Master the most popular tools like Tensorflow, Keras, PyTorch, H2O, etc.
  • Describe how organizations are leveraging automated machine learning through case studies

For a completed program description, download the program's brochure


Spring Quarter 2022:

  • Application Deadline:Mar. 10, 2022
  • Tuition Deadline:Mar. 17, 2022
  • Classes Begin:Mar. 30, 2022
  • Classes End:June 8, 2022

Meeting Pattern

Online: Hybrid Section

Days & Times: Wednesdays, 5:45pm-9:00pm (Central Time)

These classes are a combination of asynchronous and synchronous online work. The class meets synchronously on-line, on specified dates at the assigned time, using a synchronous meeting tool like Zoom. Classes will be recorded, so any student who is not able to join at the specified time will be able to watch the recording of the class. Though the class has assigned time(s) and day(s) of the week, it typically meets synchronously only some of the time.

Synchronous online classes will meet on six (6) Wednesdays: Mar. 30, Apr. 13, Apr. 27, May 11, May 25, and June 8, 2022. A two- to three-hour pre-recorded lecture will be assigned for viewing on the weeks that there are no synchronous online meetings.

What if I cannot make it to every online synchronous class meeting?
Joining the class meetings in real time is highly recommended, but is not required. You can still take this class by viewing the class recordings and keeping pace with the weekly topics and assignments.

Online: Async (Sync-Option) Section

These classes do not meet at a specific time on specific days. This class is paired with a class that takes place at a specific time on a specific day via Zoom (or a similar tool). Students in this class may connect with the live classroom and instructor, if and when they are available, but they are not required to do so.

Students will have access to the recordings of the paired synchronous online classroom. They are expected to follow the same weekly agenda of topics and assignments as the class that meets synchronously, but can view the course material at a time of their own choosing.


Spring Quarter 2022


  • $2,665.00

Full payment of tuition must be received before the start of the program. Students who elect to pay tuition using a credit or debit card will be assessed a non-refundable 2.75% convenience fee.

Refund/Cancellation Policy: DePaul reserves the right to cancel any program before that program’s first scheduled meeting, in which case tuition fees (but not convenience fees) will be refunded. The university's refund policy allows a return of 100% of tuition if the student drops the Machine Learning and Deep Learning Program by April 13, 2022 (convenience fees will not be refunded).

Notice for Current DePaul Students

  • Undergraduates: Please be aware that the tuition fee for this program is not included in the university’s full-time term package pricing.

Application Fee

  • $40.00non-refundable

Each program requires a $40.00 (non-refundable) application fee that can be paid online (via credit card) during the online application process. If you need to pay this fee by check or money order, please make the check or money order payable to DePaul University and send it to:

DePaul University Institute for Professional Development
243 S. Wabash Avenue
Room 301
Chicago, IL 60604


Textbooks are a separate purchase to be made by students.

Reading materials for certificate programs consist of textbooks and supplementary handouts. Textbook readings are considered preparatory in nature and are typically assigned prior to lectures; supplementary handouts are frequently distributed in class to provide additional information.

Textbooks TBA

Payment Options

Fees are payable by check made out to DePaul University, or by credit card. Students who elect to pay tuition using a credit or debit card will be assessed a non-redundable 2.75% convenience fee.

Applicants who are eligible for a tuition reimbursement program offered by their employer and are interested in deferring their tuition payment using the university's Employer Tuition Deferral Plan must return the Employer Tuition Deferral Plan application to the Institute for Professional Development Office. Submitting this application to any other DePaul office may delay the student's registration process. Information about this plan, along with an application form, is found here .

Applicants who wish to use the university's Single Term Payment Plan or a third-party billing arrangement should contact the Institute for Professional Development office at (312) 362-6282 for details.


The Institute for Professional Development is pleased to announce a new need-based financial award, the Chou Family IPD Scholarship, to provide funding for students interested in our certificate programs in Spring Quarter 2022. Applications will be accepted through Mar. 11, 2022 for this scholarship.  


Admission Requirements

The program is suitable for data scientists, business analytics and other IT professionals who already have a basic understanding of machine learning concepts. Basic experience with Python programming is also required. Experience using cloud services is assumed. In addition, students must bring their own laptop computers running either Windows or Mac OS to class.

Course Credit

The Machine Learning and Deep Learning Program is catalogued as a non-credit course of DePaul University. A certificate of completion from DePaul University is awarded to those who successfully complete the program's requirements. Program requirements include reading assignments, labs, projects, case studies, and group discussions. No midterm or final exams are conducted.

Course #: IPD 242

The Machine Learning and Deep Learning Program is a graded course. A final grade letter as well as a DePaul transcript (upon request) will be available upon program completion.

Application & Registration Procedure

All interested parties should apply for admission using the Institute for Professional Development's online application; or, download and complete the Application Form and email to Upon admission, the Institute office will contact the prospective student with information and instructions about the registration process.

You do not have to be an existing DePaul student to take this certificate program. Registration is restricted to individuals who apply for admission to the program and receive an acceptance letter. IPD staff will register applicants upon receipt of payment and registration form.

Regular DePaul students cannot register themselves via the university's registration system. If interested in enrollment, regular DePaul students should begin by submitting an application for admission. Students must meet the program's admission criteria.

programmers orking together

Learn about the new, need-based financial award, the Chou Family IPD Scholarship.


What Our Students Say About IPD Programs

"Professor was great. He's very knowledgeable & it is good to know that he's also working in the field. Professor deserves more than 10s."

"The class was very well organized for online students. The instructor was very helpful via email. The topics were well presented."

"Program was great overall. I have taken multiple IPD courses at DePaul and have been happy with all."