Montreal College of Information Technology
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Machine Learning

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.

Schedule: Monday, Wednesday, Friday - 18h00 to 21h00


  • Machine Learning

    Get trained by Industry experts

    Our courses are delivered by professionals with years of experience having learned first-hand the best, in-demand techniques, concepts, and latest tools.
  • Machine Learning

    Official Certification curriculum

    Our curriculum is kept up to date with the latest official Certification syllabus and making you getting ready to take the exam.
  • Machine Learning

    Tax Credit

    Claim up to 25% of tuition fees and education tax credit from your taxes.
  • Machine Learning

    Discount on Certification Voucher

    Upto 50 percent discount voucher will be provided.
  • Machine Learning

    24/7 Lab access

    Our students have access to their labs and course materials at any hour of the day to maximize their learning potential and guarantee success.


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.

This module covers data preprocessing and exploration, emphasizing techniques for preparing data for machine learning tasks. It includes handling missing values, data normalization, encoding categorical variables, and exploratory data analysis (EDA) techniques like visualization and statistical analysis to comprehend data structures and patterns.

This module focuses on supervised learning algorithms, exploring models that learn from labeled data pairs. It covers various algorithms such as linear regression, logistic regression, decision trees, support vector machines (SVM), k-nearest neighbors (KNN), and ensemble methods like random forests and gradient boosting, providing a comprehensive understanding of supervised learning techniques.

This module delves into unsupervised learning algorithms, which operate on unlabeled data. It covers techniques like clustering (K-means, hierarchical clustering), dimensionality reduction. Unsupervised methods aim to reveal patterns, structures, or relationships within data without predefined labels, fostering a comprehensive understanding of data properties and relationships.

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.

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.

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.

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.

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.



The machine learning certification course is ideal for anyone who wants to pursue a career in data science, machine learning, or artificial intelligence. It is suitable for those with background in mathematics and programming, including software developers, data analysts, business analysts, and IT professionals.
It is relevant for professionals in other fields who want to transition into data science or machine learning roles. Ultimately, anyone who is interested in learning how to develop and deploy machine learning models can benefit from this certification course.
Aimed at professionals who deal with large amounts of Data in their jobs on a daily basis to help organizations understand trends and take critical decisions.
Academic achievers who are just out of universities. This program will help add competencies to their portfolio.

Eligibility and Requirements

Learners need to possess an undergraduate degree or a high school diploma. 



Knowledge on Statistics and Python programming are required to enroll in the course.

Machine Learning Certification.


Upon completing this certification course you will:

  • Receive an industry-recognized certificate from MCIT.
  • Be prepared for any real time certification related to Machine Learning.



— F.A.Q —

All of our exceptionally skilled instructors have a decent experience of training and industry experience and are AW certified in the respective field. Each of them through a rigorous selection procedure that included profile screening, technical examination, and a training demo. 
Yes, there are vouchers to take the official exam.
Definitely. Please feel free to contact our office, we will be more than happy to work with you to meet your training needs.
Upon completion of the certification course classes you will be provided with an MCIT certificate.