Machine Learning
Master industry-standard tools and techniques such as Search Engine Optimization (SEO), Social Media Marketing, Google Ads, Conversion Optimization, Google Analytics, digital content marketing, marketing automation, and front-end web development.
Algorithimic thinking, Problem solving, Debugging
Duration
36 hours
Schedule : Mon - Wed - Thu
6 PM - 9 PM
Skills gained
Modular thinking,
Programming basics, Algorithimic thinking,
Problem solving
Debugging
Tools
Java, ASP,
Microsoft .Net, SQL server
Object Oriented
Programming, ORM,
Pricing
Course Fee ; 2500
Bundle price available
Request Corporate
vouchers
Program Overview
The Machine Learning Course is a comprehensive and practical introduction to the field of machine learning. Designed for both beginners and those with some prior experience, this course covers the key concepts, algorithms, and techniques used in machine learning. Through a combination of theoretical learning, hands-on coding exercises, and real-world projects, participants will gain a deep understanding of machine learning and its applications.
Enroll
Start Date
Duration
Tuition Fee
36 hrs
CAD 2500
14 June 2025
Key Benefits
Official Accredited
Curicullum
Our curriculum is kept up to date with the latest official Certification syllabus and making you getting ready to take the exam.
Get trained by
Industry experts
Our courses are delivered by professionals with years of experience
in the industry.
24/7
Labs
Our students have access to their labs and course materials at any hour of the day to maximize their learning potential and guarantee success.
Certification
Vouchers
Upto 50 percent discount voucher will be provided.
Tax
Credits
Claim up to 25% of tuition fees and education tax credit from your taxes.
Course Outline
Introduction to Machine Learning
This module initiates the understanding of machine learning, emphasizing its significance in problem-solving and technology. It delves into the distinctions among supervised, unsupervised, and reinforcement learning paradigms. Additionally, it showcases machine learning's versatile applications across diverse industries, providing a comprehensive outlook on its real-world implementations.
SKILLS GAINED
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Data Preprocessing and Exploration
Supervised Learning Algorithms
Unsupervised Learning Algorithms
Model Evaluation and Validation
This module focuses on model evaluation and validation techniques in machine learning. It covers methods like cross-validation, train-test splits, performance metrics (accuracy, precision, recall, F1-score), and learning curves. It aims to assess model performance, reliability, and generalizability, ensuring robustness and accuracy in predictive models.
SKILLS GAINED
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Neural Networks and Deep Learning
This module delves into neural networks and deep learning, exploring multi-layered networks capable of learning complex patterns. It covers artificial neural networks, deep learning architectures (CNNs, RNNs), activation functions, optimization techniques (SGD, Adam), and frameworks like TensorFlow or PyTorch. Deep learning enables modeling intricate data structures, making it integral in various fields for tasks like image recognition, natural language processing, and more.
SKILLS GAINED
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Computer Vision
This module centers on computer vision, encompassing techniques to enable computers to interpret and understand visual information from images or videos. It covers image processing, feature extraction, object detection, segmentation, and recognition using methods like CNNs (Convolutional Neural Networks) and frameworks such as OpenCV. Computer vision finds applications in fields like autonomous vehicles, healthcare imaging, security, and augmented reality.
SKILLS GAINED
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Model Deployment and Ethics
This module focuses on the deployment of machine learning models into real-world applications and the ethical considerations surrounding their usage. It covers techniques for deploying models in production environments, ensuring scalability, efficiency, and reliability. Additionally, it delves into ethical considerations, addressing issues like bias, fairness, transparency, and privacy, fostering responsible and ethical AI implementations.
SKILLS GAINED
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Advanced Topics and Future Trends
This module explores advanced topics and future trends in the realm of machine learning and artificial intelligence. It may cover cutting-edge techniques like generative adversarial networks (GANs), transfer learning, reinforcement learning advancements, and emerging trends in AI research. Additionally, it might delve into topics such as AI ethics, explainable AI, and the integration of AI with emerging technologies, providing insights into the evolving landscape of machine learning and its potential future directions.
SKILLS GAINED
…………………………………………………………………………………………………………………………………………………………
Skills gained
Algorithimic
Thinking
Programming
basics
Modular
Thinking
Problem
Solving
Debugging
Instructor Spotlight
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Eligibility Criteria
Learners need to possess an undergraduate degree or a high school diploma. No need of any professional experience is required as this is the fundamental course.
Prerequisites
Knowledge on Front End Technologies, SQL Programming is required to enroll in this course.
Upcoming sessions & Schedule
Enroll
Summer Session
17 June 2025
Register before
30th May 2025
Enroll
Fall Session
17 October 2025
Register before
30th May 2025
Enroll
Winter Session
12 February 2026
Register before
30th May 2025
Enroll
Enroll for this
Certification
20th June
Start Date
CAD 2500/-
Stand alone Certification
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pricing
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SQL + BI + Python Programming
6000 CAD
Bundle price for three
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Related programs
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FAQs
About the Instructor?
Is there any Voucher to take the Official certification?
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When do I get the MCIT Certificate?
Upon completion of the certification course classes you will be provided with an MCIT certificate.
Montreal College of Information Technology
Collège des technologies de l’information de Montréal
200-1255 Robert-Bourassa Blvd.
Montreal, Quebec H3B 3B2
+1 514 405 6874
info@montrealcollege.ca