Introduction to Machine Learning
Feb 26, 2024 /
Course Code: 14-0235-ONL24
This course is held online over 1 day on the following schedule (All times in Eastern Time Zone):
10:00 am to 6:00 pm Eastern (Will include the usual breaks)
After participating in this course, you will:
- Be knowledgeable of the fundamentals of machine learning, its terminology, and applications
- Be able to differentiate amongst several machine learning techniques and when each one is to be used
- Have a good understanding of Neural Networks and how they are built, trained, and evaluated
Machine learning has become a critical tool for businesses to gain insights from data, make informed decisions, and drive innovation. This specialized course is designed for managers and decision-makers who want to understand the principles of machine learning and its practical applications in a business context.
Throughout this course, participants will gain a solid foundation in machine learning concepts, learn how to leverage machine learning for business problems and explore real-world case studies. It's designed to provide a solid foundation for non-computer scientists to understand and engage with machine learning concepts and applications.
- Introduction to Machine Learning
- Fundamentals of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Evaluating Machine Learning Models
- Introduction to Neural Networks
Who Should Attend
This course is suitable for professionals, students, and individuals interested in machine learning and its applications. It caters to beginners with no prior experience in the field, as well as those with some knowledge looking to expand their understanding and practical skills.
Special Features and Requirements
No specific technical background or programming knowledge is required. However, a basic understanding of data analytics concepts and familiarity with business processes will be beneficial.
Time: 10:00 AM - 6:00 PM Eastern Time
Please note: You can check other time zones here.
Introduction to Machine Learning
- What is machine learning?
- Real-world applications and impact of machine learning
Fundamentals of Machine Learning
- Types of machine learning: supervised, unsupervised, and reinforcement learning
- Key components: data, features, labels, models, and predictions
- Basic terminology: training, testing, and evaluation
- Overview of supervised learning
- Classification vs. regression problems
- Example algorithms: decision trees, logistic regression, and support vector machines
- Overview of unsupervised learning
- Clustering algorithms: k-means, hierarchical clustering
Evaluating Machine Learning Models
- Metrics for model evaluation: accuracy, precision, recall, F1-score
- Overfitting and underfitting
- Cross-validation and model selection
Introduction to Neural Networks
- Basics of artificial neural networks
- Building blocks: neurons, layers, activation functions
Deep learning and its applications
Dr. Yasser Ebrahim obtained his Ph.D. in Computer Science from the University of Guelph in 2006. He has a Master’s in Computer Science degree from the University of Waterloo (2003) besides a Master’s degree from DePaul University (1995) in Computer Information Systems.
For the past 22 years, Dr. Ebrahim has taught at Wilfrid Laurier, McMaster University, Ryerson University, and the University of Toronto in Mississauga. He has taught various computer science courses, including programming in C/C++, object-oriented programming in Java, Python, data structures, software architecture, operating systems, database systems, human-computer interaction, software engineering, and computer graphics.
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Fee & Credits
$525 + taxes
- 0.7 Continuing Education Units (CEUs)
- 7 Continuing Professional Development Hours (PDHs/CPDs)
- ECAA Annual Professional Development Points
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