Python has become the de facto language for machine learning due to its user-friendly nature, robust ecosystem, and widespread adoption within the community. Learning how you can use Python for machine learning tasks will give you a competitive edge in the job market and provide you with the necessary tools to excel in this rapidly evolving field.
AI and machine learning jobs have jumped by almost 75% over the past four years.
Source: John Terra, Simplilearn, July 7 2023
Before you jump into machine learning though, it’s important to know the basics. This course is an essential starting point for machine learning with an approach that is accessible and rooted in practical value. You’ll learn vital pre-machine learning skills, with a focus on data preparation, to avoid common data science risks, like “garbage in, garbage out.”
Perfect for beginners and professionals looking to advance into roles that include machine learning, this course is designed to introduce you to Python, a programming language known for its simplicity and readability. Python has an extensive library that includes packages specifically designed for machine learning, such as NumPy, Pandas, Scikit-learn, SciPy, and Statsmodels. Through a series of modules, you’ll gain hands-on experience with Python's libraries, syntax, data structures, and functions relevant to machine learning tasks.
Upon completion of the course, you’ll have a solid understanding of proper data preparation and how to get the results you’re seeking.
- Explain the practical benefits and uses of machine learning: Gain an understanding of the real-world applications of machine learning and its
significance in various fields such as business, healthcare, finance, marketing, and
more. Recognize the difficulties with operationalizing machine learning models and
build a broader understanding of the applicability of machine learning.
- Create a machine learning portfolio in Jupyter Notebook to show potential employers:
Learn how to set up and configure your Python environment using Jupyter Notebook,
leveraging the Anaconda distribution platform. Explore the benefits of Jupyter Notebook
for interactive coding, data exploration, and documentation, and understand how Anaconda
simplifies package management and environment creation.
- Identify key components of data preparation in machine learning: Understand the crucial role of data preparation in machine learning pipelines. Identify
key components such as data cleaning, missing values, feature scaling, categorical
variable encoding, and common manipulations. Recognize the significance of data preprocessing
for improving model performance and achieving reliable results.
- Gain hands-on experience with some of Python's packages and libraries: Learning the Python libraries and packages NumPy, Pandas, Scikit-learn, SciPy, and Statsmodels is crucial for a well-rounded proficiency in data science and machine learning. From data handling and preprocessing to modeling and analysis, these libraries and packages are the building blocks of modern data-driven decision-making and advanced research.
- Great for those without experience with Python!
- Individuals in non-technical roles with basic programming and data preparation knowledge, aiming to gain practical, in-demand machine learning and AI skills to stay competitive in the evolving workforce.
- Aspiring data analysts, data scientists, or statisticians who are new to machine learning or looking to build their toolkits.
- Programmers and developers looking to add Python to their list of programming languages.
Learn alongside your team!
WatSPEED provides custom learning experiences tailored for large groups from any single organization. Register three or more employees from the same organization and receive 15 per cent off. Contact our team at watspeed@uwaterloo.ca for details.
- Exclusive access to your program author—a University of Waterloo faculty expert in machine learning.
- Learn at your own pace with weekly independent online learning and hands-on exercises.
- Attend a live orientation session before your course starts to get up to speed on the curriculum.
- Optional live drop-in sessions twice weekly via Zoom where you can ask questions and
receive instructor support on key course concepts:
- Wednesdays, 2:30 - 3 p.m. ET
- Wednesdays, 7:30 - 8 p.m. ET
- Engage directly with your classmates through online discussion boards.
- Up to five hours each week (including drop-in sessions, hands-on exercises, discussion
boards, and independent study).
Academic requirements
- Basic coding skills in any programming language (it does not need to be Python)
System requirements
- Anaconda (software that you are required to install)
Receive a certificate from the University of Waterloo
Upon successful completion of this program, you will receive a professional education certificate from the University of Waterloo.
This course forms part of a comprehensive, Machine Learning Practitioner Certificate.
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.
Fuat Can Beylunioglu
PhD Candidate, Faculty of Engineering | Program Instructor
Fuat Can Beylunioglu is a PhD candidate at the University of Waterloo's Faculty of Engineering, where he has also earned his master's degree. His research interests include developing explainable neural network models to solve challenging optimization problems, improving interpretability and safety of so-called black-box systems, and applying these techniques to reliable power systems.
Prior to his PhD studies, Fuat Can has published several papers on various topics, including search engines, AI applications in digital marketing, and macroeconometrics. He is currently working as a researcher in Waterloo Institute of Sustainable Aviation and is actively contributing to open-source projects related to AI in health care.
Manda (Hongxiu) Li
Senior Machine Learning Engineer, Block | Program Contributor
Dr. Manda Li is an accomplished machine learning professional and researcher with a profound passion for machine learning applications and statistical analysis. She obtained her PhD in Economics from the University of Waterloo in 2017, focusing her research on climate change, innovation, and empirical modeling. With a robust background in statistics, modeling, and machine learning, Manda has extensive industry experience in the finance, insurance, and banking sectors. This practical expertise enables her to bridge real-world challenges with the content covered in the machine learning courses, providing students with valuable insights into the practical applications of their studies.
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