Ammar - Software teacher - Montréal
1st lesson free
Ammar - Software teacher - Montréal

One of our best tutors. Quality profile, experience in their field, verified qualifications and a great response time. Ammar will be happy to arrange your first Software lesson.

Ammar

One of our best tutors. Quality profile, experience in their field, verified qualifications and a great response time. Ammar will be happy to arrange your first Software lesson.

  • Rate L344
  • Response 2h
  • Students

    Number of students Ammar has accompanied since arriving at Superprof

    50+

    Number of students Ammar has accompanied since arriving at Superprof

Ammar - Software teacher - Montréal
  • 5 (15 reviews)

L344/hr

1st lesson free

Contact

1st lesson free

1st lesson free

  • Software
  • Machine learning
  • Coding

Master Machine Learning, AI, Python and Predictive Modelling with a PhD Engineer and Professor | 25+ Years’ Expertise | Beginner to University, Research and Professional Levels

  • Software
  • Machine learning
  • Coding

Lesson location

Ambassador

One of our best tutors. Quality profile, experience in their field, verified qualifications and a great response time. Ammar will be happy to arrange your first Software lesson.

About Ammar

I am a multidisciplinary educator, researcher, engineer, trainer, and consultant with more than 25 years of experience in research, technology, professional training, and applied problem solving.
My academic background includes a Bachelor’s degree in Engineering, a Master’s degree in Management Information Systems, and a PhD in Knowledge Management and Artificial Intelligence. This combination allows me to connect AI theory with data, information systems, research, engineering, business, and real decision-making contexts.
Over the years, I have completed hundreds of research and technical projects and supported or supervised hundreds of university learners, researchers, and professionals. I am experienced in translating complex technical ideas into clear, structured explanations adapted to different backgrounds and levels.
For Artificial Intelligence specifically, I combine conceptual foundations with machine learning, model evaluation, knowledge representation, symbolic AI, and applied intelligent-system reasoning. Depending on the objective, I can work with tools and approaches involving Python-based AI workflows, Weka, Prolog, OWL, Lisp, ChatGPT, prompt engineering, and modern Generative AI concepts. I emphasize critical evaluation, limitations, and responsible use - not merely obtaining an impressive-looking output.
I teach in English, French, and Arabic and adapt my explanations to the learner’s academic background, profession, technical confidence, software environment, and goals.

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About the lesson

  • Primary
  • Lower Secondary
  • Senior Secondary
  • +15
  • levels :

    Primary

    Lower Secondary

    Senior Secondary

    Post Secondary Education

    Higher Education

    Adult Education

    Master's Degree

    MBA

    Early Childhood Care & Development

    Beginner

    Intermediate

    Advanced

    Professional

    Children

    Doctorate

    Other Extracurricular

    Post Graduate Diploma

    Other

  • French
  • English

All languages in which the lesson is available :

French

English

Artificial intelligence becomes much easier when you understand what a model is doing, why a method is appropriate, how it learns from data, how performance should be evaluated, and where its limitations begin. My lessons are built around that principle: understand the reasoning first, then connect it to models, tools, and real applications.
I support university and college students, graduate researchers, professionals, managers, and motivated learners who want structured guidance in artificial intelligence, machine learning, predictive modeling, Generative AI, ChatGPT, prompt engineering, and intelligent systems.
Depending on your goals, we can work on:
• Artificial intelligence foundations: intelligent agents, problem framing, search, reasoning, and the relationship between AI, machine learning, deep learning, and Generative AI
• Supervised learning: regression and classification from a predictive-modeling perspective
• Unsupervised learning: clustering, dimensionality reduction, pattern discovery, and representation concepts
• Data preprocessing for models, feature selection and engineering, training/validation/test design, and prevention of data leakage
• Overfitting and underfitting, bias-variance reasoning, regularization concepts, hyperparameter tuning, and cross-validation
• Model evaluation: confusion matrix, accuracy, precision, recall, F1 score, ROC-AUC, calibration, regression metrics, and selecting metrics that match the real objective
• Model selection and comparison, baselines, error analysis, interpretability, explainability, and defensible conclusions
• Neural-network and deep-learning foundations when relevant to your level or course
•Natural language processing, computer vision, recommendation, and other AI application areas at the appropriate conceptual or applied level
• Generative AI and large language model foundations: tokens, context, embeddings, hallucinations, retrieval concepts, model limitations, and responsible use
• ChatGPT and prompt engineering: clear task definition, context design, constraints, examples, structured outputs, iterative refinement, evaluation, and workflow integration
• Symbolic AI, knowledge representation, expert systems, rules, ontologies, Prolog, OWL, Lisp, and related approaches when relevant
• Applied AI workflows using appropriate tools such as Python-based environments, Weka, notebooks, or other platforms when the learning objective requires them
• Responsible AI: bias, privacy, transparency, validation, human oversight, ethical risks, and the difference between persuasive output and reliable evidence
Every session is adapted to your actual objective. We may use your course notes, syllabus, model output, research question, professional use case, AI tool, or carefully designed examples. I explain concepts visually, numerically, conceptually, and through practical scenarios so that you can connect theory to application.
My goal is not to make you dependent on a particular AI tool or library. I want you to understand the logic of the system, recognize weak assumptions and common failure modes, evaluate outputs critically, and become progressively independent.

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Rates

Rate

  • L344

Pack prices

  • 5h: L1,721
  • 10h: L3,442

online

  • L344/h

Travel

  • + L10

free lessons

The first free lesson with Ammar will allow you to get to know each other and clearly specify your needs for your next lessons.

  • 1hr

Details

Getting started: You may begin directly with a paid tutoring session when the topic and objective are already clear. If you prefer to discuss your needs first, we can have a brief free Zoom meeting - together with a parent or guardian when relevant - to clarify the level, goals, and best learning plan. No booking or payment is required for this introductory meeting;

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