Machine Learning or Python Programming courses (all levels) - Longueuil

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Machine Learning or Python Programming courses (all levels)

From 32 £ /h

Machine Learning and Data Science are very advanced fields and, as such, in fashion. They are major tools for any new technology and the school is lagging behind in teaching these skills.

In addition, these skills are theoretical as well as practical skills, and the multiple online courses focus on practice, forgetting that companies are not only looking for performers, but also experts in the intelligent use of these tools.

Having advanced theoretical training in this field, along with more than 2 years of field experience in information programming associated with machine learning, I propose to teach you this subject, both theory and practice, at the option of courses combining the two aspects of the thing.

Extra information

No training, theoretical or practical, is required. But I will adapt to the knowledge of the student and, if he / she does not know anything in applied mathematics or programming, the amount of time to plan will be important.

Location

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At student's location :

Around Longueuil, 10, Canada

About Me

- I am a young Frenchman of 25, in Montreal for work and enthusiastic about the idea of discovering Quebec and the people of Quebec!
- Coming from French fields of excellence in theoretical and applied mathematics, I pushed the love of mathematics to the point of teaching, during private lessons and preparatory classes, mathematics at the highest level
- I have trained many students in oral mathematics competitions, with an emphasis not on magical methods but on pedagogy, to help students form reasoning, understand demonstrations and apply them. My students have always recommended me for my patience and my pedagogy.

Education

Preparatory classes at the Lycée Saint Louis (Paris)
Master Grande Ecole, HEC Paris
Master in Data Science / Advanced Statistics, ENSAE Paris
2 years of experience in Data Science applied to insurance and finance

Experience / Qualifications

Private lessons for 4 years, at a rate of 3 students per year, 2 hours per week
Oral high-level mathematics exams, for 3 years

Age

Teenagers (13-17 years old)

Adults (18-64 years old)

Seniors (65+ years old)

Student level

Beginner

Intermediate

Advanced

Duration

60 minutes

90 minutes

120 minutes

The class is taught in

French

English

Skills

Statistics

Computer programming

Availability of a typical week

(GMT -04:00)

New York

At student's home

Mon

Tue

Wed

Thu

Fri

Sat

Sun

00-04

04-08

08-12

12-16

16-20

20-24

Mathematics courses for all levels, theory and practice

32 £ /h

A former student of HEC Paris and ENSAE Paris (School of Applied Mathematics and Statistics of the Institut Polytechnique Paris), I did high-level mathematics and now work as a data scientist and statistician in the service of financial institutions.

My course in France is one of the most demanding in mathematics and I was an oral interrogator for preparatory classes in France, preparing students for oral mathematics for the competition. In France, I gave hundreds of hours of private lessons but also, therefore, in preparatory classes. I teach all levels, up to the most advanced. My students generally recommend me for my pedagogy and my ability to explain complex reasoning graphically, and therefore clearly.

I prefer teaching in the presence, even if it is possible to teach by webcam.

Florian

4.67

(3)

Developer since 10 years, I work on the development of new 3D engine for different industries. My experience ranges from raytracer to standard OpenGL rendering. The languages covered will be C / C ++, OpenGL and OpenCL.

This course is for anyone wanting an introduction to this type of programming or wanting to learn and discuss developments and new methods in this area. The focus is mainly on the use of graphic resources and understanding how to use the material.

For beginners in programming, the course will focus on learning the technical terms, methodologies and concepts namely to program effectively in general.

Ahmed

4.67

(3)

Mathematics, Physics, and Computer Science Tutor | Montreal | French & English
Private tutoring in mathematics, physics and chemistry, life and earth sciences, and computer science for high school, CEGEP, and university students in M

French curriculum: middle school, high school, preparation for the Baccalaureate (Mathematics, Physics-Chemistry, Life and Earth Sciences) — Stanislas, Marie de France
Quebec Program (Secondary & CEGEP), (NYA, NYB, NYC), university

Mathematics: Secondary 1 to 5 (including SN and CST components).
Science: Secondary 5 Physics and Chemistry.
CEGEP: Integral and Differential Calculus (NYA, NYB), Linear Algebra (NYC), and Physics.

English-language program: secondary school, CEGEP, university level
Computer science: Java, C++, Linux, algorithms
formations

Baccalaureate with a specialization in Mathematics
B.Sc. Computer Science, Finance and Mathematics — McGill
M.Sc. Applied Computer Science — Concordia

I have been giving private lessons in mathematics, physics-chemistry and computer science for over 10 years in Montreal. I support high school, CEGEP and university students, in Quebec, French and English programs.
In mathematics and physics, I teach from secondary school to university level, including CEGEP courses at NYA, NYB, and NYC. For students at French schools in Montreal such as Stanislas or Marie de France, I cover the French curriculum from middle school through the Baccalaureate with a specialization in Mathematics, including mathematics, physics and chemistry, and life and earth sciences.
In computer science, I teach programming courses in Java, C++ and Linux, as well as algorithm courses for college and university levels.
My method is based on understanding before memorization. Each session is adapted to the student's level and objectives, whether it is to fill gaps in knowledge, prepare for an exam or deepen understanding of a concept.
My background is rooted in both systems: I graduated with a French Baccalaureate specializing in Mathematics, hold a B.Sc. in Computer Science-Finance-Mathematics from McGill University, and an M.Sc. in Applied Computer Science from Concordia University. I have over 10 years of experience tutoring students of all levels in mathematics, physics, and computer science in Montreal.

Sébastien

4.67

(3)

Hello,

I am a trainee engineer at MBA and I have 19 years of experience in the field. I teach web and mobile programming courses (Spring, Java, Hibernate, Angular, HTML5, CSS3, etc.)

I am available on Saturdays.

thank you,

Manoosh

4.67

(3)

I have a bachelor's degree in Electrical Engineering- Telecommunications from SBU university in Iran. SBU is one of the top 5 universities in Iran. I was always among the top three students during my undergrad. I am specifically good at Math, Programming, and Electrical Circuits analysis. During my undergrad, I was a TA for AVR micro-controllers programming and probability & statistics courses, during which I gained lots of teaching experience. During my bachelor's thesis, I implemented Behavioral Cloning (end-to-end) approach for self-driving by programming Artificial Neural networks in python with Keras and Tensorflow frameworks. I am currently a master student in the ECE department of McGill University working in the field of Computer Vision at Visual Motor Research Lab and am a member of Center for Intelligent Machines (CIM) at McGIll.

I believe that learning is only effective when you have a question in mind. Thus, I always try to first stimulate student's curiosity on the subject and talk about its application, before teaching that subject to them. Also, I teach the subjects very slowly and step by step to allow students to think deeply about everything I teach to them. Also, my courses' syllabus is flexible and I usually consult them with students on the first session.

4.67

(3)

Engineer and senior professor of engineering sciences provides support courses in analog and digital electronics at all levels, engineering schools. having a scientific and technical knowledge, five years of experience in the field of teaching, teaching and a sense of listening and analysis, I am able to help pupils and students and train them in the chapters of which they have difficulties. for more info please contact me

Keivan

4.67

(3)

Dr. Keivan is a McGill University graduate with the following degrees:
Master of Mechanical Engineering (McGill)
Bachelor of Mechanical Engineering (McGill)
Doctor of Medicine M.D (Iran)
Dr. Keivan has more than 15 years’ experience in teaching many MATH, ENGR, and MECH courses for University students. He has been teaching assistant for many courses at Concordia University and McGill University in Montreal, with excellent course evaluation by students. The most featured courses are undergraduate and graduate mechanical engineering courses, and probability and statistics. Both in person and online classes are offered.
For more information, you can contact Dr. Keivan at (514)4762075
Concordia Courses:
COMM 215: Business Statistics
COMP 233: Probability & Statistics
ECON 325: Mathematics for Economists I
ECON 326: Mathematics for Economists II
ELEC 275: Principles of Electrical Engineering
ENGR 213: Applied Ordinary Differential Equations
ENGR 233: Applied Advanced Calculus
ENGR 242: Statics
ENGR 243: Dynamics
ENGR 244: Mechanics of Material
ENGR 251: Thermodynamics I
ENGR 264: Signals and Systems I
ENGR 273: Basic Circuit Analysis
ENGR 301: Management Principals and Economics
ENGR 311: Calculus and Partial Differential Equations
ENGR 351: Thermodynamics II
ENGR 361: Fluid Mechanics I
ENGR 371: Probability and Statistics
ENGR 391: Numerical Methods
INDU 371: Random Processes
INTE 296: Discover Statistics
MATH 201: Elementary Functions
MATH 202: College Algebra
MATH 203: Differential and Integral Calculus I
MATH 204: Vectors and Matrices
MATH 205: Differential and Integral Calculus II
MATH 206: Algebra and Functions
MATH 208: Fundamental Mathematics I
MATH 209: Fundamental Mathematics II
MATH 251: Linear Algebra I
MATH 252: Linear Algebra II
MATH 264: Advanced Calculus I
MATH 265: Advanced Calculus II
MECH 211: Mechanical Engineering Drawing
MECH 215: Programming for Mechanical and Industrial
MECH 221: Material Science
MECH 313: Machine Drawing and Design
MECH 361: Fluid Mechanics II
MECH 368: Electronics for Mechanical Engineers
MECH 370: Modeling and Analysis of Dynamic Systems
MECH 371: Fundamentals of Control Systems
MECH 375: Mechanical Vibrations
MECH 6121: Aerodynamics
PHYS 204: Mechanics
PHYS 205: Electricity and Magnetism
PHYS 206: Waves and Optics
PSYC 315: Statistical Analysis I
PSYC 316: Statistical Analysis II
SOCI 212: Statistics I
SOCI 213: Statistics II
STAT 249: Probability I
STAT 250: Statistics
STAT 360: Linear Models

McGill Courses:
CIVE 205: Statics
CIVE 206: Dynamics
CIVE 207: Solid Mechanics
CIVE 290: Thermodynamics and Heat Transfer
CIVE 302: Probabilistic Systems
CIVE 320: Numerical Methods
CIVE 327: Fluid Mechanics and Hydraulics
ECON 208: Microeconomics Analysis and Applications
ECON 227: Economic Statistics
MATH 112: Fundamentals of Mathematics
MATH 122: Calculus for Management
MATH 123: Linear Algebra and Probability
MATH 133: Linear Algebra and Geometry
MATH 139: Calculus I with Pre-calculus
MATH 140: Calculus I
MATH 141: Calculus II
MATH 150: Calculus A
MATH 203: Principles of Statistics I
MATH 204: Principles of Statistics II
MATH 222: Calculus III
MATH 223: Linear Algebra
MATH 262: Intermediate Calculus
MATH 263: Ordinary Differential Equations for Engineers
MATH 270: Applied Linear Algebra
MATH 271: Linear Algebra and Partial Differential Equations
MATH 315: Ordinary Differential Equations
MATH 316: Complex Variables
MATH 323: Probability
MATH 324: Statistics
MATH 329: Theory of Interest
MECH 210 Mechanics I
MECH 220 Mechanics II
MECH 240 Thermodynamics I
MECH 289 Design Graphics
MECH 290: Design Graphic for Mechanical Engineers
MECH 309: Numerical Methods in Mechanical Engineering
MECH 314: Dynamics of Mechanisms
MECH 315: Mechanics III
MECH 361: Fluid Mechanics I
MECH 341: Thermodynamics II
MECH 346: Heat Transfer
MECH 383: Applied Electronics and Instrumentation
MECH 393: Machine Element Design
MECH 412: System Dynamics and Control
MECH 419: Advanced Mechanics of Systems
MECH 430: Fluid Mechanics II
MECH 513: Control Systems
MECH 533: Subsonic Aerodynamics
MECH 542: Spacecraft Dynamics
MECH 562: Advanced Fluid Mechanics
MECH 605: Applied Math I
MECH 642: Advanced Dynamics
MGCR 271: Business Statistics
MGSC 372: Advanced Business Statistics
PHYS 101: Introductory Physics – Mechanics
PHYS 102: Introductory Physics – Electromagnetism
PHYS 131: Mechanics and Waves
PHYS 142: Electromagnetism and Optics
PSYC 204: Introduction to Psychological Statistics
PSYC 305: Statistics for Experimental Design

Mohamed

4.67

(3)

The aim of this course is to learn programming in general and to discover the different programming applications such as machine learning, deep learning or even video game programming via Unity.
There is also the possibility of doing lessons at a more advanced level according to the student's need and to concentrate on one point or another.

Ludovic

4.67

(3)

I've been developing since I was 15, and coding has always been at the heart of my career. Initially, Java was my language of choice. Now I use Python and Nodejs. I developed a complete product which was sold to companies in order to offer Single Sign-On (SSO) to an Oracle product supporting OpenID, SAML and Kerberos. I developed in Node.js.

Amin

4.67

(3)

Are you a university student, engineer, or professional who needs to actually use data — not just learn theory about it?
This course is built around real problems and real code. We skip the textbook formulas and go straight to applying statistics and data science the way professionals do: with Python (pandas, NumPy, scikit-learn, matplotlib) and R (RStudio).
What we cover, adapted to your level and goals:
- Descriptive and inferential statistics (the ones that actually matter)
- Data cleaning, exploration, and visualization
- Regression, classification, and intro to machine learning
- Time series and forecasting basics
- R for statistical analysis and academic research

Who this is for:
- University students in statistics, economics, engineering, or biology
- Professionals wanting to move into data analysis or data science
- Researchers who need to process and present data properly

I use Python and R professionally as a working engineer — everything I teach comes from real application, not just academic exercises.

Lucas

4.67

(3)

This course aims to provide a solid foundation of programming concepts and techniques, using popular programming languages such as C, C++ or Python. This course is suitable for people who are new to programming or who already have some basic knowledge and want to improve their skills. Whether you want to become a software developer, get into data analysis, or simply learn to code, this course will provide you with the essential knowledge and practical skills needed to succeed.

Aniket

4.67

(3)

Hello,
I'm doing a PhD in AI and ML using Python and am an Oracle-certified trainer with 350+ reviews and ratings [with proof attached], I will be able to teach you Python better than any of my competition.

Why choose me?
1. 300 + reviews and ratings
2. Certified tutor
3. More than 5 years of teaching experience
4. Worked as a Software engineer in companies like Virtusa Corp and DIGIDEZ DIGITAL SYSTEMS
5. Hold B.tech and M.tech in Computer Science

Featured Review :
Been trying to learn Java on my own for about 1 year and I couldn't get a grasp on it. Aniket make learning Java a fun experience and challenges you to think for yourself to reinforce the concepts you've learned. I am truly excited for our meetings and he makes time go by so fast that I'm upset when they end. Great teacher and he is genuinely passionate about your success. If I could give him more stars I would!!!

Thanks
Aniket

Etienne

4.67

(3)

Hello,
My name is Etienne and I am a final year student in a dual engineering school degree. I have already been a private tutor for 3 years, and I love passing on my knowledge! I am bilingual in English (985/990 on the TOEIC), and have a Master's level in Mathematics. I can also give science or computer science lessons. We can plan a face-to-face, distance or hybrid course.
I hope to see you again soon!

Nathanaël

4.67

(3)

Having graduated with a master's degree in industrial engineering, with a major in computer science at Polytechnique Montréal, I would like to give math and/or computer science courses to students in a university program, at CEGEP or at secondary school.
During my studies at Polytechnique Montréal, I gave classroom lessons, practical work (around 50 people), as well as mathematics reinforcement for all types of profiles (individual help).
I also have previous private tutoring experience.

It is always a real pleasure for me to witness the success of the students and to see their progress session after session.
I insist on stimulating students' thinking so that they are as effective as possible during their exams.

It would be a pleasure to have a first meeting!

Mathilde

4.67

(3)

For:
- Better understand your science courses (math, physics, chemistry, biology, computer science)
- Find effective working methods that suit you
- Regain confidence in your abilities
- Discover that science can become exciting

I offer personalized courses adapted to each profile which go beyond simple academic support:

✅ Learning to learn (organization, memorization, reasoning)
✅ Develop solid and sustainable methods
✅ Work at your own pace, with kindness

An engineer in medical imaging, neuroscience, and artificial intelligence, my rigorous scientific background and my passion for sharing my knowledge drive me to support students in their success. My goal is to give students a taste for science and the keys to becoming independent and confident. I adapt to the pace and needs of each individual, combining rigor and kindness to restore self-confidence and rediscover the joy of learning, essential for progress.

Ammar

4.67

(3)

A- SUJETS QUE VOUS POUVEZ EXPLORER ET MAÎTRISER :

1- FONDEMENTS DE PYTHON
• Variables, types de données, opérateurs, structures conditionnelles, boucles, fonctions, modules, fichiers, exceptions et programmation orientée objet
• Listes, tuples, dictionnaires, ensembles, compréhensions, débogage et écriture d’un code clair, réutilisable et bien structuré
• Jupyter Notebook, Anaconda, Visual Studio Code, environnements virtuels et gestion des packages

2- PRÉPARATION ET EXPLORATION DES DONNÉES
• NumPy et pandas pour importer, nettoyer, transformer, filtrer, regrouper, restructurer et fusionner les données
• Valeurs manquantes, doublons, valeurs aberrantes, formats incohérents, fuite de données (data leakage) et validation de la qualité des données
• Analyse exploratoire des données à l’aide de statistiques descriptives, Matplotlib, Seaborn et interprétation graphique

3- FONDEMENTS MATHÉMATIQUES
• Algèbre linéaire, vecteurs, matrices, dérivées, optimisation, probabilités et statistiques
• Fonctions de perte, gradients, mesures de distance, régularisation, vraisemblance et complexité des modèles
• Les concepts mathématiques sont expliqués en fonction du niveau de l’apprenant et des exigences des algorithmes sélectionnés

4- APPRENTISSAGE AUTOMATIQUE SUPERVISÉ
• Régression linéaire et polynomiale, régression logistique et modèles régularisés
• k plus proches voisins (k-nearest neighbours), arbres de décision, forêts aléatoires, gradient boosting, machines à vecteurs de support et classificateur naïf de Bayes
• Classification, régression, hypothèses des modèles, frontières de décision, importance des variables et interprétation des résultats

5- APPRENTISSAGE NON SUPERVISÉ
• Regroupement (clustering) par k-means, classification hiérarchique et méthodes fondées sur la densité
• Analyse en composantes principales, réduction de dimensionnalité, détection d’anomalies et découverte de structures ou de motifs
• Sélection des méthodes, évaluation de la structure des données et interprétation des résultats sans étiquettes prédéfinies

6- ÉVALUATION ET AMÉLIORATION DES MODÈLES
• Jeux d’entraînement, de validation et de test ; validation croisée ; optimisation des hyperparamètres
• Exactitude (accuracy), précision, rappel (recall), spécificité, score F1, ROC–AUC, matrices de confusion, MAE, MSE, RMSE et R2
• Sous-apprentissage, surapprentissage, compromis biais–variance, déséquilibre des classes, ingénierie des variables, sélection des variables, mise à l’échelle et régularisation

7- APPRENTISSAGE PROFOND
• Fondements des réseaux de neurones, fonctions d’activation, propagation avant, rétropropagation et descente de gradient
• Perceptrons multicouches, réseaux de neurones convolutifs, réseaux récurrents et fondements des Transformers
• TensorFlow, Keras ou PyTorch selon le projet et l’environnement de travail de l’apprenant

8- APPLICATIONS DE L’INTELLIGENCE ARTIFICIELLE
• Traitement automatique du langage naturel, classification de textes, plongements vectoriels (embeddings), analyse de sentiments et fondements des modèles de langage
• Vision par ordinateur, classification d’images, principes fondamentaux de la détection d’objets et prétraitement des images
• Systèmes de recommandation, prévision, détection d’anomalies, automatisation intelligente et applications d’aide à la décision

9- IA GÉNÉRATIVE ET GRANDS MODÈLES DE LANGAGE
• Architecture Transformer, tokens, embeddings, mécanismes d’attention, ingénierie des prompts, génération augmentée par récupération (Retrieval-Augmented Generation – RAG) et évaluation des modèles
• Utilisation d’API d’intelligence artificielle, de bases de données vectorielles, de systèmes de recherche documentaire et de flux de travail structurés utilisant l’IA lorsque cela est pertinent
• Fiabilité, hallucinations, biais, confidentialité, utilisation responsable et validation humaine appropriée

10- OUTILS ET BIBLIOTHÈQUES
• Python, NumPy, pandas, Matplotlib, Seaborn, scikit-learn, SciPy, Statsmodels, TensorFlow, Keras et PyTorch
• Jupyter Notebook, Anaconda, Visual Studio Code, Git, GitHub, SQL, Excel et Power BI lorsqu’ils sont utiles au projet
• Des bibliothèques supplémentaires peuvent être introduites en fonction de la spécialisation choisie et du jeu de données utilisé

11- PROJETS, RECHERCHE ET PRÉPARATION AUX ENTRETIENS
• Projets complets couvrant la préparation des données, le développement des modèles, leur évaluation, leur interprétation et la présentation des résultats
• Travaux universitaires, mémoires, thèses, projets de recherche, projets de portfolio, entretiens techniques et applications professionnelles
• Revue de code, débogage, documentation, reproductibilité, comparaison de modèles et communication des résultats

B- TUTORAT PERSONNALISÉ : APPRENDRE À RAISONNER
L’apprentissage automatique et l’intelligence artificielle deviennent beaucoup plus accessibles lorsque les mathématiques, les algorithmes, le code Python, les données et les applications concrètes sont clairement reliés entre eux.

Mes cours vous aident à aller au-delà de la simple copie de code ou de l’utilisation de modèles comme des « boîtes noires ». Vous apprendrez à définir correctement le problème, préparer les données, sélectionner un algorithme approprié, comprendre son fonctionnement, entraîner et évaluer le modèle, diagnostiquer les erreurs, améliorer ses performances et interpréter les résultats de manière rigoureuse et responsable.

Chaque cours est personnalisé en fonction de votre niveau actuel, de vos connaissances mathématiques, de votre expérience en programmation, de votre jeu de données, de votre travail universitaire, de votre projet de recherche, de votre préparation à un entretien ou de votre objectif professionnel. Nous commençons par identifier vos connaissances existantes, votre environnement logiciel, les résultats attendus ainsi que vos principales difficultés conceptuelles ou techniques. Nous établissons ensuite un plan d’apprentissage structuré.

Le premier cours gratuit combine une discussion portant sur votre parcours, vos objectifs et vos besoins en tutorat, une première évaluation de vos connaissances actuelles, une planification et une organisation personnalisées des séances, ainsi qu’un court cours d’essai afin de déterminer la méthode de travail la plus efficace.

Une séance type peut comprendre une explication conceptuelle, le développement de l’intuition mathématique, de la programmation en direct, une mise en œuvre guidée, l’évaluation des modèles, la résolution de problèmes techniques et une synthèse concise des prochaines étapes.

Vous pouvez travailler avec votre propre jeu de données, travail universitaire, projet de recherche ou problématique professionnelle, à condition que les informations confidentielles soient traitées de manière appropriée. Je peux également fournir des exemples structurés et des jeux de données adaptés à votre niveau.

Mon objectif n’est pas simplement de vous aider à exécuter un algorithme. Il est de vous permettre de comprendre pourquoi il est approprié, comment il apprend à partir des données, comment l’évaluer correctement, pourquoi il peut échouer et comment construire une solution fiable, interprétable et scientifiquement rigoureuse.