Maîtrisez l’IA, le Machine Learning & Python avec un ingénieur PhD et professeur | 25+ ans d’expertise | Débutant à avancé

From 14 £ /h

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.

Extra information

Le premier cours gratuit est une séance d’introduction structurée comprenant également un cours d’essai. Nous commencerons par nous présenter brièvement, notamment en abordant votre parcours académique ou professionnel ainsi que mon expertise pertinente. Nous préciserons ensuite vos objectifs, vos échéances et vos besoins en tutorat, puis nous évaluerons votre niveau actuel au moyen d’une discussion et d’une courte activité diagnostique.
Nous établirons ensuite un plan d’apprentissage ciblé ainsi qu’un calendrier pour les cours suivants. Le temps restant sera consacré à un court cours d’essai portant sur un concept ou un problème représentatif, afin de vous permettre de découvrir concrètement mon approche pédagogique avant de décider si vous souhaitez poursuivre.

Location

Use Ctrl + wheel to zoom!

At student's location :

Around Laval, 10, Canada

Online from Canada

About Me

I am a PhD-qualified engineer, university professor, researcher, and multidisciplinary tutor with more than 30 years of experience in teaching, training, mentoring, research, engineering, and technology.

I enjoy helping students move from confusion to genuine understanding. My approach is structured, patient, personalized, and concept-driven: I first identify your goals and the real source of difficulty, then explain the underlying ideas clearly using visual, numerical, and real-world examples before moving to guided practice and independent problem solving.

I work with teenagers, university students, graduate researchers, engineers, professionals, and adult learners. My areas include statistics, probability, research methods, quantitative analysis, data science, mathematics, physics, chemistry, programming, engineering, CAD/BIM/3D modelling, GIS, and project management.

I do not simply provide formulas, software commands, or final answers. My goal is to help you understand why a method works, when to use it, how to verify the result, and how to apply the same reasoning confidently to new problems.

Whether you are strengthening your foundations, preparing for an exam, analyzing data, conducting research, learning technical software, or solving an advanced engineering problem, I adapt each lesson to your level, objectives, and pace. I value serious learning, curiosity, open communication, and a respectful environment where questions are always welcome.

Education

Bachelor of Applied Science in Mechanical Engineering, Engineering Management Option — University of Ottawa, Canada, 1990. Graduated Magna Cum Laude.

Master of Business Administration (MBA), Management Information Systems / Business Intelligence — Jinan University, 2004. Rank: Very Good. Graduate research focused on data mining for business applications, including clustering, decision trees, and neural networks.

PhD in Management Information Systems (Knowledge Management) — Jinan University, 2008. Rank: Excellent. Doctoral research focused on knowledge representation, organizational memory, ontology development, reasoning, and educational knowledge management.

My multidisciplinary education connects engineering and scientific problem solving with quantitative analysis, research, data, information systems, technology, and management.

Experience / Qualifications

More than 30 years of university teaching, professional training, mentoring, research, engineering, and consulting experience. Former Associate Professor, Dean of a Faculty of Business Administration, Vice President for Scientific Research and Higher Studies, and Vice President for Development and Technology.

Extensive undergraduate and graduate teaching experience in statistics, advanced quantitative methods, research methodology, data mining, business intelligence, mathematics, operations research, project management, construction management, database systems, management information systems, and related analytical disciplines.

Strong practical experience in statistical and data-analysis tools including SPSS, Stata, SAS, Excel, Python, and related analytical workflows; programming and information technologies; and engineering/design tools including AutoCAD, Revit, BIM workflows, 3D modelling, Primavera, MS Project, ArcGIS, and other technical software.

Professional engineering experience includes engineering analysis and design, project planning and control, CAD-based technical work, GIS and spatial analysis, infrastructure-related studies, engineering software development, and multidisciplinary project consulting.

Experienced in supporting university students, graduate researchers, engineers, professionals, and adult learners with theoretical understanding, problem solving, research design, quantitative analysis, interpretation of results, software workflows, technical projects, and independent skill development.

Age

Children (7-12 years old)

Teenagers (13-17 years old)

Adults (18-64 years old)

Seniors (65+ years old)

Student level

Beginner

Intermediate

Advanced

Duration

60 minutes

The class is taught in

English

French

Arabic

Skills

Python

Computer programming

Algorithms

Availability of a typical week

(GMT -04:00)

New York

Online via webcam

At student's home

Mon

Tue

Wed

Thu

Fri

Sat

Sun

00-04

04-08

08-12

12-16

16-20

20-24

Master Research, Thesis & Professional Projects | Methodology, Project Management, Career & Interviews

14 £ /h

Complex academic and professional goals become manageable when the objective is clear, the methodology is sound, the work is properly planned, and decisions are based on structured reasoning rather than improvisation.

I am a PhD-qualified engineer, university professor, published researcher, former senior academic leader, project practitioner, and multidisciplinary mentor with more than 30 years of experience across teaching, research, supervision, engineering, project planning, management, professional development, and decision support.

This class provides personalized guidance for university students, graduate researchers, engineers, professionals, managers, career changers, and adult learners. Depending on your objective, we can focus on one of three clearly defined pathways or connect them when your situation genuinely requires an integrated approach.

RESEARCH METHODS, THESIS & DISSERTATION
• Defining and narrowing a research problem
• Developing research questions and objectives
• Formulating hypotheses
• Building conceptual and theoretical frameworks
• Literature-review strategy and source evaluation
• Connecting theories, constructs, variables, and measurement
• Quantitative research design
• Qualitative research design
• Mixed-methods research
• Experimental, observational, survey, and case-study approaches
• Population and sampling decisions
• Sample-size considerations
• Questionnaire and survey design
• Reliability and validity
• Operationalization of variables
• Coding plans and data preparation
• Research ethics and responsible data handling
• Selecting appropriate analytical methods
• Developing a coherent data-analysis plan
• Interpreting quantitative and qualitative findings
• Structuring methodology and results chapters
• Connecting findings to research questions and hypotheses
• Discussion, limitations, implications, and recommendations
• Responding systematically to supervisor feedback
• Preparing to explain and defend methodological decisions

PROJECT MANAGEMENT & PROFESSIONAL EXECUTION
• Project objectives and success criteria
• Scope definition and requirements
• Work Breakdown Structure (WBS)
• Activity definition and sequencing
• Network diagrams
• Critical Path Method (CPM)
• PERT and schedule uncertainty
• Milestones and deliverables
• Resource planning and allocation
• Cost estimation and budgeting concepts
• Project scheduling and control
• Risk identification, analysis, and response planning
• Stakeholder analysis
• Communication planning
• Quality and performance monitoring
• Change management
• Traditional, Agile, and hybrid approaches
• Construction and engineering project contexts
• Microsoft Project workflows
• Primavera planning and scheduling
• Diagnosing delayed or underperforming projects
• Turning complex objectives into executable action plans

CAREER STRATEGY & INTERVIEW PREPARATION
• Clarifying career direction and professional objectives
• Identifying transferable skills
• Skills-gap analysis
• Career-transition planning
• Professional positioning and value proposition
• CV and résumé strategy
• Matching experience to job requirements
• Preparing for behavioral interviews
• Preparing for technical and analytical interviews
• Structuring evidence-based answers
• STAR and other response frameworks
• Developing strong professional examples and stories
• Mock-interview practice
• Diagnosing weak or unclear answers
• Communicating complex experience concisely
• Preparing for questions about strengths, weaknesses, conflict, leadership, failure, and problem solving
• Interview preparation for academic, technical, engineering, analytical, and management roles
• Building a realistic professional-development plan

My approach follows a common structured logic:
define the objective → diagnose the current situation → identify constraints → select the appropriate methodology → build the plan → execute → monitor → evaluate → communicate the result → improve

We can work with your research proposal, thesis plan, supervisor feedback, conceptual framework, questionnaire, methodology chapter, project schedule, WBS, risk register, MS Project or Primavera file, CV, job description, interview questions, or professional-development challenge.

I do not simply provide generic templates or ready-made answers. I help you understand why a method or strategy fits your situation, what assumptions it depends on, how to evaluate alternatives, how to detect weaknesses, and how to defend the final decision clearly.

Academic and professional integrity are essential. I provide teaching, methodological guidance, critical feedback, analytical support, planning, coaching, and supervision-style mentoring. I do not write assessed theses or dissertations, complete examinations, fabricate research results, or misrepresent a student’s or professional’s experience.

My goal is to help you become the genuine owner of your research, project, or professional path—able to explain your decisions, manage complexity, communicate clearly, and move forward independently.

Master Mathematics, Statistics & Data Analysis | Calculus, Probability, Econometrics & Visualization

14 £ /h

Mathematics, statistics, and data analysis become much easier when formulas, reasoning, computation, and real-world interpretation are connected clearly.

I am a PhD-qualified engineer, university professor, researcher, and multidisciplinary tutor with more than 30 years of experience in teaching, quantitative methods, mathematical problem solving, statistical analysis, research, engineering, data analysis, and professional decision support.

This class provides a structured and personalized learning pathway for school and university students, graduate researchers, engineers, professionals, and adult learners. Depending on your goals, we can focus on one specific area or connect several areas into a coherent program.

MATHEMATICS
• Arithmetic, fractions, ratios, percentages, and mathematical foundations
• Algebraic expressions, equations, inequalities, and systems
• Functions, graphs, and transformations
• Geometry and analytic geometry
• Trigonometry
• Precalculus
• Limits and continuity
• Differential calculus and applications
• Integral calculus and applications
• Sequences and series
• Multivariable calculus
• Linear algebra, matrices, vectors, and systems
• Differential equations
• Numerical methods
• Applied and engineering mathematics

STATISTICS, PROBABILITY & ECONOMETRICS
• Descriptive statistics
• Probability rules and probabilistic reasoning
• Random variables and probability distributions
• Sampling and sampling distributions
• Confidence intervals
• Hypothesis testing
• Correlation and regression
• Multiple regression
• ANOVA, ANCOVA, and MANOVA
• Nonparametric methods
• Multivariate statistical analysis
• Econometrics and quantitative methods
• Time-series analysis and forecasting
• Mediation and moderation analysis
• Statistical modelling and predictive analysis

DATA ANALYSIS & VISUALIZATION
• Data organization and quality assessment
• Data cleaning and preparation
• Missing values, duplicates, inconsistencies, and outliers
• Exploratory data analysis
• Summary tables and analytical reporting
• PivotTables and aggregation
• Data visualization and appropriate chart selection
• Trend and pattern analysis
• KPI development and performance analysis
• Dashboard concepts and decision-support reporting
• Research and survey data preparation
• Interpretation and communication of analytical findings

Depending on your needs, practical work may involve Excel, SPSS, Stata, R, SAS, Power BI, SQL, Python, or other relevant analytical tools. Software is never treated as a substitute for understanding: I explain the reasoning behind the method, the assumptions involved, the meaning of the output, and how to verify whether the conclusion is sound.

My teaching approach follows a clear progression:
understand the problem → identify the appropriate concept or method → develop the reasoning → calculate or analyze → verify the result → interpret it → communicate the conclusion

We can work with your course syllabus, textbook, representative exercises, exam topics, dataset, statistical output, research question, spreadsheet, dashboard, engineering application, or professional analytical problem.

Whether you are rebuilding mathematical foundations, preparing for an examination, studying advanced calculus, learning statistics, conducting econometric analysis, interpreting research data, or developing practical analytical skills, I will adapt the sessions to your level, objectives, and pace.

My goal is not merely to help you obtain an answer, but to help you understand the reasoning, choose appropriate methods, verify results, interpret findings correctly, and solve new problems independently.

Jean-François

3.0

(2)

Homework help.
Exam preparation.
Preparing for job interviews.
You can attend the course alone or with a group of 5 people maximum.

Florian

3.0

(2)

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

3.0

(2)

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.

3.0

(2)

Hello, I am a doctoral student in electrical engineering and associate professor in engineering sciences, experienced in the field of electrical engineering, I offer support courses in the subjects of engineering sciences (Electronics, automatics, electrical engineering, automation, programming).

Digital electronics
Analog electronic
electromagnetism (propagation of high frequency waves)
Automatic (continuous, sampled)
electrical engineering (transformers, electrical machines, switching power supply)
C / c ++ programming, Assembler, ARM, STM32
renewable energy (wind, PV)
engineering Sciences
RDM
Python,VHDL
PIC Microprocessor and Microcontroller
Signal processing and data acquisition
Engineering Sciences

These courses allow the student to get up to speed and regain confidence in all scientific subjects, just as they prepare him effectively for the Baccalaureate, the Preparatory Classes or various examinations of the engineering classes.

COURSE OBJECTIVES AND PEDAGOGICAL APPROACH

Resumption and deepening of fundamental concepts through exercises with course reminders.

Put the student in a situation of questioning and research.

Respond to individual issues and questions

Exercise training in order to achieve real mastery of the content.

Learn to build theoretical reasoning from observable facts or hypotheses.

Specific preparation for higher education requirements (in-depth content, increase in work capacity, enrichment of scientific background)

This educational approach is effective since it has often led me to interesting results with my students.

Associate professor provides support courses in electrical engineering

Sébastien

3.0

(2)

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

3.0

(2)

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.

Adam

3.0

(2)

Learning to program is not just about writing code. It's about learning to analyze a problem, construct a line of reasoning, and develop effective solutions.

For over 35 years, I have been supporting university students, engineering school students and adults retraining in learning computer science and programming.

Whether you are a beginner or preparing for an exam, a university project or a technical interview, I adapt to your level and your objectives.

Subjects taught
Python
Java
SQL and databases
Algorithmic
Data structures
Object-oriented programming (OOP)
Program design and debugging
What we work on together
Understanding fundamental concepts rather than memorizing code.
Develop a problem-solving method.
Correct and improve your programs.
Prepare for practical work, projects and exams.
Acquire good programming practices used in higher education and in business.
A pedagogy based on practice

Each session alternates between explanations, demonstrations, and exercises. We write, test, and debug the code together so that you understand not only how to program, but more importantly, why a solution works.

When it's helpful, I also show you how to use programming assistance tools thoughtfully, including AI-powered assistants. The goal isn't to let AI program for you, but to teach you how to verify, understand, and improve the solutions it provides.

Session Procedure

60-minute session

Ideal for solving a specific problem, understanding a difficult concept, or correcting a program.

90-minute session

Recommended for a university project, a complete refresher course or exam preparation.

My commitment

My goal is for you to gradually become independent. At the end of each session, you should be able to understand your code, explain your choices, and continue your work with greater confidence.

I will be happy to support you in your progress, whatever your starting level.

Dorian

3.0

(2)

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.

Mohamed

3.0

(2)

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.

Amin

3.0

(2)

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.

Ludovic

3.0

(2)

Introduction: Master AI Agent Development from A to Z
This intensive course immerses you in the heart of developing modern, autonomous, and communicative Artificial Intelligence Agents. You will learn to build sophisticated agent systems capable of cooperating, using external tools, and interacting via dedicated user interfaces. It's the ideal training to progress from simple AI scripting to the complete architecture of intelligent agents.

What you will learn:
Frontend Agent (AG-UI): Create a dynamic and intuitive user interface specifically designed to interact with and view the status of your AI agents.

Agent Architecture (ADK): Master the Agent Development Kit (ADK) to structure, program and deploy your agents, giving them autonomy and decision-making capabilities.

Agent-to-Agent (A2A) Communication: Implement secure and efficient communication protocols to enable your agents to collaborate, share information, and form intelligent teams.

Tool Consumption (MCP): Learn how to connect your agents to the Multi-Capability Platform (MCP) so they can interact with external tools, services, and APIs, extending their capabilities beyond their internal code.

Who should attend ?
Software developers and engineers wishing to specialize in AI agent architectures.

AI architects seeking to understand and implement complex multi-agent systems.

Anyone passionate about AI and eager to build autonomous agent applications.

Prerequisite:
Basic knowledge of Python (recommended).

OPTIONAL (Adaptation): If you are a beginner in Python, the course will be adapted to include the basics of the language through the practical implementation of ADK concepts. You will learn Python by building your first agents!

Course Format:
The course combines essential theory and intensive practice with exercises and a final project to build a complete agent system.

Aniket

3.0

(2)

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

3.0

(2)

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

3.0

(2)

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

3.0

(2)

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.