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Professeur fiable
Ce professeur a un délai et un taux de réponse très élevé, démontrant un service de qualité et sa fidélité envers ses élèves.
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Depuis septembre 2021
Professeur depuis septembre 2021
Machine Learning (ML) – Think “training” an AI to make predictions. (Used in trading bots, recommendations, etc.)Generative AI – Like ChatG
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Àpd 29.96 $ /h
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⚙️ Step 1: Understand What AI Actually Is
AI isn’t magic — it’s just pattern recognition on steroids. It learns from data, spots trends faster than humans, and automates decisions.
There are three main flavors you’ll be dealing with:
Machine Learning (ML) – Think “training” an AI to make predictions. (Used in trading bots, recommendations, etc.)
Generative AI – Like ChatGPT or Midjourney; it creates text, code, or images.
Automation AI – Uses rules + ML to perform actions automatically (Zapier, n8n, AutoGPT, etc.).
💸 Step 2: Using AI in Trading
AI trading isn’t new — hedge funds have been doing it for decades. But now you can do it too.
Here’s the stack you’d look at:
Data Source – Pull data from APIs (like Binance, Alpha Vantage, or Polygon.io).
Model – Use AI to find signals. Common tools:
Python + TensorFlow/PyTorch → train models that predict price movement.
GPTs → analyze news sentiment.
Execution – Automate trades using APIs (like Binance or Alpaca).
Testing – Backtest with historical data before going live.
If you want plug-and-play options, try:
Koyfin, TrendSpider, or Trade-Ideas for AI-driven analysis.
Composer or Stoic.ai if you want “autopilot” portfolios.
(Pro tip: the real edge isn’t in fancy models — it’s in feeding your AI clean, unique data.)
📱 Step 3: Using AI to Build Apps
This is where things get fun. You don’t even need to code hardcore anymore.
No-code builders: Glide, Bubble, or Adalo → use AI APIs like OpenAI, HuggingFace, or Replicate to add brains.
AI dev tools: Replit Ghostwriter, GitHub Copilot, or ChatGPT’s code interpreter to write real code fast.
Frameworks (if coding):
Python/Flask or FastAPI for backend.
React or Next.js for frontend.
Firebase or Supabase for hosting & data.
Hook it all together with AI APIs (like OpenAI, Anthropic, or Stability AI).
🚀 Step 4: Learn by Doing (Don’t Just Watch YouTube)
The fastest way to master AI is to build real stuff:
Create a sentiment analyzer that trades crypto based on tweets.
Build an AI app that generates marketing copy for clients.
Automate your own work — make an AI that handles your emails or schedules.
📚 Step 5: Resources That’ll Level You Up
Learn ML basics: fast.ai or Google’s ML Crash Course.
Build AI apps: freeCodeCamp or YouTube’s “Code with Tomi” / “Nicholas Renotte.”
Trading AI: Check out “Algorithmic Trading with Python” by QuantInsti.
No-code AI apps: Buildspace or FlowiseAI.
Informations supplémentaires
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Lieu
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En ligne depuis Belgique
Age
Adolescents (13-17 ans)
Adultes (18-64 ans)
Seniors (65+ ans)
Niveau du Cours
Débutant
Intermédiaire
Avancé
Durée
45 minutes
60 minutes
90 minutes
Enseigné en
néerlandais
anglais
persan
Disponibilité semaine type
(GMT -05:00)
New York
at teacher icon
Cours par webcam
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
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Vous avez des données mais ne savez pas comment les exploiter ? Vous souhaitez prendre des décisions basées sur des faits concrets ? Ou vous êtes étudiant·e et voulez maîtriser les outils de l'analyse moderne ?
Ce cours est fait pour vous.

👨‍🏫 À propos du formateur :
Je suis Data Scientist et Ingénieur en Mathématiques Appliquées, diplômé de l’Université Cheikh Anta Diop (UCAD). Mon expertise repose sur une solide base en Mathématiques, Statistiques, Machine Learning et Visualisation de données. J’allie rigueur scientifique et outils modernes pour transformer des données brutes en décisions stratégiques.

🧠 Objectifs du cours :
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- Identifier les variables importantes et repérer les anomalies

- Appliquer les méthodes statistiques et Machine Learning pour extraire de la valeur

- Construire des tableaux de bord clairs et parlants pour la prise de décision

- Adapter les analyses aux besoins réels d’une entreprise ou d’un projet académique

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1. Introduction à l’analyse de données

- Qu’est-ce que l’analyse de données ?

- Typologie des données (quantitatives, qualitatives)

- Méthodologie globale

2. Préparation des données

- Nettoyage (valeurs manquantes, doublons, outliers)

- Encodage des variables catégorielles

- Normalisation et transformation

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- Graphiques de distribution, de corrélation, de tendance

- Tableaux croisés, heatmaps, boxplots

- Détection de patterns et d’anomalies

4. Statistique descriptive et inférentielle

- Moyenne, Médiane, Ecart-type, Corrélation

- Tests statistiques : Khi2, t de Student, ANOVA

5. Modélisation prédictive (ML supervisé)

- Régression linéaire/logistique

- Arbre de décision, Random forest, KNN, SVM

- Évaluation : accuracy, recall, precision, F1-score, AUC

6. Segmentation et classification non supervisée

- Clustering (K-means, DBSCAN, hiérarchique)

- Réduction de dimension (ACP/PCA)

7. Projets réels (au choix)

- Analyse des ventes / Churn client / Scoring de crédit / Santé publique

- Ou projet personnalisé à vos propres données

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- Python (Pandas, Matplotlib, Scikit-learn, Seaborn)

- ou R (selon la préférence)

- Excel, Power BI/Tableau (pour la visualisation avancée)
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* Creation of sites with WordPress (installation, themes, extensions, pages, basic SEO)
* Support for your personal or professional projects
* Advice on best practices and organization of your code

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* Complete beginners
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* Anyone curious to learn how to create a website

💡 Method: Interactive video lessons (Zoom/Meet), screen sharing, practical exercises, and flashcards. You progress at your own pace, with real support and simple explanations.

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Create and manipulate objects in JavaScript in a clean and efficient way
Use ES6 classes, constructors, and methods with confidence
Mastering this, the prototype, and the instantiation logic
Apply encapsulation, inheritance, and polymorphism without confusion
Avoiding common mistakes made by OOP beginners
Structure your JavaScript code like a professional developer

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1. Introduction to Object-Oriented Programming 🧠
Understanding the concept, objectives and benefits of OOP.
2. Procedural Programming vs. OOP
Why unstructured code quickly becomes unmanageable.
3. Objects in JavaScript
Properties, methods and representation of the real world.
4. The keyword this
Understanding the execution context (often poorly understood).
5. Limitations of simple objects
Why duplicating code is a bad idea.
6. Constructive functions
Create multiple objects from the same model.
7. The keyword new
What it's actually doing under the hood.
8. The prototype
Sharing methods and memory optimization.
9. ES6 Classes
Modern syntax and best practices.
10. The builder
Proper initialization of objects.
11. Data Encapsulation
Protect the internal state of objects.
12. Inheritance between classes
Reusing code intelligently.
13. The keyword super
Communication between parent and child in the classroom.
14. Polymorphism
The same behavior, several forms.
15. Composition vs. Inheritance
Choosing the right architecture.
16. Best practices in OOP
Write readable, scalable, and maintainable code.
17. Common mistakes made by beginners
Pitfalls to absolutely avoid.
18. Guided practical exercise
Creation of a concrete class (product, user, etc.).
19. Assessment Quiz (Multiple Choice Questions)
To validate the actual understanding of the concepts.

🛠️ Teaching method: Understand before writing

This training program is based on a progressive and pragmatic approach:
Clear and illustrated explanations
Concrete examples from real projects
Simple but effective exercises
Constant questioning to avoid rote learning
Adaptation to the learner's level and pace
Here, we don't "recite OOP" — we understand it.

🚀 Learner's result

At the end of the training, you will not only know how to write a JavaScript class.
You will know:

1- Why does it exist?
2- When to use it
3- and when not to use it

You will leave with:
a solid understanding of OOP
a cleaner and more professional code
an ideal foundation for learning React, Node.js or any other modern framework
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Python is today one of the most in-demand programming languages in the world, used in software development, data analysis, artificial intelligence, and automation.

This course is designed to guide you step by step, whether you're a beginner or looking to deepen your skills. My approach is practical and project-oriented: you'll learn by coding.

In the program :
• Python basics (variables, conditions, loops)
• Functions and modular programming
• Lists, tuples, dictionaries
• Object-oriented programming
• File manipulation
• Introduction to NumPy and Pandas (data analysis)
• Concrete mini-projects adapted to your level

This course is aimed at students, engineers, professionals or anyone wishing to develop solid skills in Python programming.

The sessions are personalized according to your objectives: academic success, exam preparation, university projects or professional development.
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I offer computer science and programming courses for young people (upper primary, middle school, high school) who want to learn to code practically and create their own projects.

During lessons, the student learns to:
- Understanding the logic of programming
- Write your first real programs in Python
- Create a website with HTML & CSS
- Add interactivity with JavaScript
- Structure your code properly
- Solve problems step by step

We don't just do theory!
Each concept is applied through small, concrete projects, for example:
- Create a simple mini-game
- Develop a calculator in Python
- Build a custom web page
- Understanding how websites work

The goal is for the student to truly understand what they are doing, gain autonomy, and develop genuine computer logic.

The courses are adapted to the level: complete beginner or student who already has a basic understanding.
Garantie Le-Bon-Prof
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