Unit 5 Coding
Unit 5 Coding
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    • Home
    • About Us
    • Courses
      • Python Basics
      • AI Libraries
      • Data Preprocessing
      • Machine Learning
      • Deep Learning
      • Large Language Models
      • Cybersecurity
    • Contact Us
  • Home
  • About Us
  • Courses
    • Python Basics
    • AI Libraries
    • Data Preprocessing
    • Machine Learning
    • Deep Learning
    • Large Language Models
    • Cybersecurity
  • Contact Us

Master Data Preprocessing

About the Course

  • An indepth introduction to data preprocessing for AI
  • Learn to clean, transform, and prepare raw data for analysis
  • The learning experience is enhanced by several examples and problem sets that will be solved interactively during the lecture with the participation of the students.
  • Many examples will be given throughout the lecture to reinforce the material learned.
  • The course format includes 3 hours of daily lectures (2 hours of lecture and 1 hour of practical practice). 

Course Level: Introduction

Fee: $$

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Materials Included

Materials Included

Materials Included

  • Live Virtual Classrooms
  • 3-Hour Sessions
  • Certificate Upon Successful Completion
  • Electronic Copies of the Course Materials
  • Access to Recorded Lectures
  • Career, and Skills Development Advice

Audience

Materials Included

Materials Included

  • Professional with introductory Python knowledge
  • Professionals who are interested in pursuing Data Science and AI
  • Professionals who are interested in designing applications in Machine Learning and Deep Learning
  • Professionals who create workflows and run operational initiatives
  • Executives, Directors, and Decision makers within companies and government

Course Requirements

What Will You Learn?

What Will You Learn?

  • Basic Knowledge of Python
  • Basic Knowledge of AI Libraries

What Will You Learn?

What Will You Learn?

What Will You Learn?

  • Have the fundamental concepts of programming using libraries to have the confidence to apply other libraries.
  • Gain the necessary knowledge to implement AI applications using the latest AI libraries.

Session 1: Intro to Data Preprocessing

 Learn the essential techniques for cleaning and preparing raw data to ensure high-quality inputs for analysis and machine learning models. 

Session 2: Dimensionality Reduction

Discover methods to simplify complex datasets by reducing features, improving model efficiency, and enhancing computational performance. 

Session 3: Image and Text Preprocessing

Master the processes of transforming raw image and text data into formats suitable for machine learning, ensuring better model accuracy and performance. 

About the Instructor

Dr. Alireza Sadeghian


Dr. Alireza Sadeghian has been with the Department of Computer Science at Toronto Metropolitan University since 1999, where he holds the position of the Professor. He is also an Affiliate Scientist at the Li Ka Shing Knowledge Institute, St. Michael's Hospital, and served as the research Theme Lead on Healthcare AI and Analytics at the Institute for Biomedical Engineering, Science, and Technology (iBEST).


Dr. Sadeghian was the Chair of the Department of Computer Science from 2005 to 2015. He is the founding Director of the Advanced Artificial Intelligence Initiative (AI2) Laboratory, Computational Intelligence Initiative (CI2) and Ubiquitous and Pervasive Computing Laboratories (UPCL), and has extensive expertise in the areas of AI, machine learning, Deep Learning, and modeling of complex dynamical systems particularly related to industrial and medical applications. He has supervised and trained 9 postdoctoral fellows, 8 PhD, and 24 Master’s students, as well as 60 research assistants. He has published over 150 journal manuscripts, refereed conference papers, and book chapters, as well as two edited books. He has also filed 2 invention disclosures and 2 patents.


Dr. Sadeghian has been actively involved with a number of international professional and academic boards, including IEEE Education Activity Board and NAFIPS board (North American Fuzzy Information Processing Society). Presently, he is the Chair of IEEE Computational Intelligence Technical Society Chapter, Toronto Section. Dr. Sadeghian is also on the Editorial Board of Applied Soft Computing Journal and serves as an Associate Editor of IEEE Access, Information Sciences, and Expert Systems Journal. He has served on over 80 conferences as Honorary Chair/General Chair/Organizer/Technical/Track Program Committee member, and has been a reviewer of many granting bodies including NSERC, MITACS, OCE, CFI, PRECARN, and PREA.


Alex Dela Cruz


 Alex Dela Cruz is a Ph.D. in Computer Science focused on machine learning and its application in medical care at Toronto Metropolitan University. He is the recipient of several scholarships and awards, including the Queen Elizabeth II Graduate Scholarship in Science and Technology and the Ontario Graduate Scholarship. He is an active volunteer with IEEE Toronto, fulfilling a position as Computational Intelligence Society Vice-Chair and IEEE Toronto Student Activities Chair for 5+ years. Alex Dela Cruz has 15+ years of experience in software development and 10+ years of experience in AI, providing many AI seminars and workshops to researchers in the Greater Toronto Area.

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