Whether you work in operations, business intelligence or marketing communications, are a recent math or science graduate, or want to change or advance your career, this certificate will open up the in-demand field of big data.
This three-course program gives you the opportunity to learn from experts about Python, supervised and unsupervised machine learning, neural networks, and practical machine leraning applications. You will cover content essential for the toolbox of a machine learning, including neural networks and deep learning; programming languages and software used in data extraction and analysis; and data security, compliance, and privacy issues.
Upon completion of this certificate you will be able to demonstrates a strong foundation in machine learning, covering essential supervised and unsupervised techniques with experience using Python for machine learning tasks.
- Professionals from every level or industry who work with analytics or data, such as:
- Business associates, operations managers, project managers, and intelligence analysts.
- Finance, securities, and insurance professionals.
- Digital marketing and communication specialists.
- Programmers and developers looking to add Python to their list of programming languages.
- Statisticians who are new to machine learning.
- Current or aspiring data analysts or data scientists looking to build a machine learning portfolio.
- Data professionals looking to add machine learning techniques to their domain.
- Individuals with basic knowledge in programming and mathematics who want to expand their machine learning knowledge and skills.
A degree in Engineering, Mathematics, or Computer Science is recommended, but not required. Basic knowledge of programming and programming languages is strongly recommended.
This certificate demonstrates that the student has built a strong foundation in machine learning, covering essential supervised and unsupervised techniques, and has experience using Python for machine learning tasks.
Learners will receive the certificate upon successful completion of a minimum of three of the following courses:
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What you will learn |
Required experience |
|---|---|---|
Python for Machine Learning
|
Great for those without experience with Python! Learn key Machine learning benefits and uses, understand essential data preparation steps, gain hands-on experience with Python's libraries, and create a portfolio in showcase your skills. |
|
Supervised Machine Learning |
Gain practical, hands-on experience using Python to implement and evaluate supervised learning algorithms to draw relevant insights and solve problems. |
|
Usupervised Machine Learning |
Explore three key concepts and practices of unsupervised machine learning—dimensionality reduction, association rules, and clustering and learn how they can be used to discover patterns and similarities in unlabelled data sets without human intervention. |
|
Neural Networks |
Learn the foundational concepts and advanced techniques of neural networks, including
RNNs, CNNs, and transformers, with hands-on experience using Python to solve real-world
problems. |
|
Mehrdad Pirnia
Continuing Lecturer, Management Sciences, Faculty of Engineering | Program Author
Dr. Mehrdad Pirnia is a faculty member at the University of Waterloo, Department of Management Sciences. Before joining Waterloo, he worked full-time in California ISO and ALSTOM Grid. He also did an internship at Federal Energy Regulatory Commission (FERC) during his PhD program.
Dr. Pirnia received his Ph.D. degree from University of Waterloo in 2014 in Electrical and Computer Engineering (Power Systems Optimization). The focus of his research is on applying AI, optimization, and stochastic techniques to enhance the operation and planning of energy systems.