Introduction to Machine Learning
Online
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Apr 23 - 24, 2025
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Course Code: 15-0411-ONL25
- Overview
- Syllabus
- Instructor
Overview
This course is held online over 2 days on the following schedule (All times in Eastern Time Zone):
9:30 am to 5:30 pm Eastern (Will include the usual breaks)
Prerequisites:
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.
After participating in this course, you will be able to:
- Understand the core principles of machine learning, including its terminology and diverse applications in various industries.
- Differentiate between several machine learning techniques and identify the appropriate context for each method.
- Develop, train, and evaluate Neural Networks, gaining hands-on experience in building sophisticated models.
- Apply effective data processing techniques to prepare datasets for machine learning applications.
- Utilize two popular machine learning platforms to construct and deploy Artificial Neural Network models for real-world problems.
Description
Machine learning has become a critical tool for businesses to gain insights from data, make informed decisions, and drive innovation. Designed for managers and decision-makers, this course focuses on understanding the principles of machine learning and its practical applications in a business context. 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. The curriculum provides a comprehensive introduction, ensuring that even those without a technical background can grasp the fundamental concepts and applications of machine learning.
Understanding machine learning is essential for anyone looking to stay competitive in today's data-driven world. By participating, you will learn the importance of machine learning in transforming raw data into actionable insights and the various techniques used to implement machine learning solutions. This knowledge empowers you to make informed decisions, optimize business processes, and drive innovation within your organization.
Throughout this course, you will explore various machine-learning techniques, understand neural networks, and get hands-on experience with popular machine-learning platforms. The curriculum is structured to build your confidence and competence in applying machine learning to real-world scenarios, making you an asset to your organization. No specific technical background or programming knowledge is required to participate. However, a basic understanding of data analytics concepts and familiarity with business processes will be beneficial.
Who Should Attend
This course is ideal for professionals eager to integrate machine learning into their business strategies. Titles of potential attendees include business analysts, data analysts, project managers, marketing managers, product managers, operations managers, IT professionals, and executives. It is also suitable for students aspiring to enter the field of data science, business analysts looking to enhance their data analytics skills, and individuals curious about the transformative power of machine learning.
The course 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. It is valuable for IT professionals aiming to transition into roles focused on data and machine learning.
More InformationTime: 9:30 AM - 5:30 PM Eastern Time
Please note: You can check other time zones here.
Syllabus
Day 1
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 concepts: data, features, labels, models, predictions, training, evaluation, and testing
Supervised Learning
- Classification vs. regression problems
- Classification Algorithms: Decision Trees, K-Nearest Neighbor, Support Vector Machines, and Artificial Neural Networks
- Regression Algorithms: Linear Regression, Polynomial Regression, Ridge and Lasso, Support Vector Regression, Decision Trees and Random Forests, and Neural Networks
- Applications
Unsupervised Learning
- Clustering, Dimensionality Reduction, and Anomaly Detection
- Clustering Algorithms: K-Means, Hierarchical Clustering, and DBSCAN
- Dimensionality Reduction: Feature Selection vs. Feature Extraction (with Principal Component Analysis)
- Anomaly Detection Algorithms: Isolation Forests and Local Outlier Factor
- Applications
Reinforcement Learning
- The Reinforcement Learning cycle
- The Policy and Learning algorithms
- Applications (ChatGPT)
Day 2
Evaluating Machine Learning Models
- Metrics for model evaluation: accuracy, precision, recall, F1-score
- Overfitting and underfitting
- Cross-validation and model selection
Artificial Neural Networks and Deep Learning
- What’s special about Deep Learning?
- Convolutional Neural Networks (CNNs)
Data Processing
- Collection, Cleaning, and Integration
- Transformation
- Feature Engineering
- Dimensionality Reduction
- Splitting
- Augmentation
Machine Learning Platforms (Demo)
- Neuroph
- TensorFlow
- Creating neural networks
- Configuring neural networks
- Learning and Testing error
- Dealing with underfitting and overfitting
Instructor

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
$1295 + taxes
- 1.4 Continuing Education Units (CEUs)
- 14 Continuing Professional Development Hours (PDHs/CPDs)
- ECAA Annual Professional Development Points
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