Montreal College of Information Technology
Collège des technologies de l’information de Montréal English flagEN FlagFR


Machine Learning

Machine learning is a subfield of artificial intelligence (AI) that involves building models and algorithms that can learn from and make predictions on data. Machine learning has revolutionized industries from finance to healthcare, and has enabled breakthroughs in image recognition, natural language processing, and autonomous vehicles. In this course, you will gain a solid understanding of the foundational concepts of machine learning, including supervised and unsupervised learning, feature selection and engineering, model selection and evaluation, and deep learning. You will also learn how to implement machine learning algorithms using popular open-source tools like Python, TensorFlow, and scikit-learn. By the end of the course, you will have developed the skills to build, train, and evaluate machine learning models on real-world datasets.

  • 7th August 2023
  • 36 hours
  • Contact the Advisor
  • Talk to an Advisor

Schedule: Monday, Wednesday, Friday - 6pm - 9pm


  • 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 provides the basic introduction of the machine learning with the concepts like Reinforcement learning, reinforcement learning, decision trees, neural networks, support vector machines, clustering, regression analysis, feature engineering, cross-validation, bias-variance tradeoff, overfitting, and underfitting are used to evaluate models.

This module explains the concepts of Linear algebra like vectors and matrices, linear transformations, eigenvalues and eigenvectors, orthogonality and projection, Singular Value Decomposition (SVD), matrix operations, determinants and rank, systems of linear equations, norms and distances, vector spaces and subspaces, statistics, probability theory, inferential statistics, correlation and regression analysis, sampling distributions, experimental design and analysis, statistical models, maximum likelihood estimation (MLE), Bayesian statistics and inference.

This module explains the concepts of the data preprocessing like data cleaning, transformation, data reduction, data discretization, and data sampling.

This module explains the Regression concepts like Linear regression, Polynomial regression, Regularization , Evaluation metrics (MSE, RMSE, R-squared, MAE) and Classification topics like Logistic regression, Naive Bayes, Decision trees, Random forests and Evaluation metrics (accuracy, precision, recall, F1-score, ROC curve, AUC).

This module explains the clustering algorithms like  K-Means, Hierarchical Clustering, Dimensionality Reduction techniques, Clustering evaluation metrics, Feature scaling and normalization and Applications of clustering and dimensionality reduction in various domains.

This module makes you to learn about the  Artificial Neural Networks (ANNs), Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning, Transfer Learning and Generative Adversarial Networks (GANs).

This module explains about the text preprocessing, Part-of-speech (POS) tagging, Sentiment analysis, Topic modeling, Language modeling, Machine translation and Text classification.

This module provides information about the time Series Data, Stationarity, Autocorrelation and Partial Autocorrelation, ARIMA Model, Exponential Smoothing and LSTM Networks.

This module explains about the concepts like Markov Decision Processes, Policy, Value Functions, Q-Learning, Exploration vs Exploitation, Reward function and Temporal Difference Learning.

This module explains about the Train/Test Split, Cross-validation, Bias-Variance Tradeoff, Evaluation Metrics, Grid Search and Hyperparameter Tuning, Ensembling and Model Selection.

This module explains about the Grid Search, Random Search, Bayesian Optimization, Cross-Validation, Overfitting and Underfitting and Performance Metrics.

This module explains the concepts like Model serving, Model versioning, Containerization, Cloud deployment, API development, Performance monitoring, Security and privacy, Scalability and Continuous integration and deployment.



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. No need of any professional experience is required as this is the fundamental course.



Statistics for Data Science is a prerequiste for this course.

Machine Learning certification.


Upon completing this cerification course you will:

  • Receive an industry-recognized certificate from MCIT.
  • Be prepared for any real time certification realted 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.