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Since June 2020
Instructor since June 2020
Translated by GoogleSee original
IT Course Level 2 for Students in French Schools
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From 73.52 $ /h
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The second year is oriented towards design and integration activities; the training allows students to confront more complex problems: information systems, real time, networks, distributed architectures, artificial intelligence, compilation, production management ...

The emphasis is on coordination within the group: work sometimes stops after the detailed design.
Extra information
Bring your computer
Location
location type icon
Online from France
About Me
Pedagogy approved for more than 30 years in the training of students, employees, job seekers, change assistance, upgrading in the largest French training companies: Bull Formation, Demos, Atos, Cegos, ...
Education
Doctorate in Deductive Databases at the University of Paris-Sud Computer Science Research Laboratory Bat 490 Orsay
Master's degree in computer science applied to management at the Institut Supérieur de Gestion
Experience / Qualifications
Doctorate in Deductive Databases at the University of Paris-Sud Computer Science Research Laboratory Bat 490 Orsay
Master's degree in computer science applied to management at the Institut Supérieur de Gestion
Age
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
French
English
Availability of a typical week
(GMT -05:00)
New York
at teacher icon
Online via webcam
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
Are you going to continue your studies in France?
In the first year of computer science, we try to ensure that students master:
the techniques,
the tools,
basic methods: design methods, algorithms, languages, operating systems, components of computing machines, etc.
Read more
PROGRAMMING

C language
C ++ language
HTML language
CSS language
JavaScript language> NodeJs> JQuery> Ajax> Json
XML language: XPath, Xslt, Xlink, Fop, Sax, Dom,
Language Java, Struts, Spring, JSF, JEE, EJB
Scala language
Language PHP, ZendFramework, CodeIgniter, Symphony, Mantis, Testmaker,
Language COBOL 1 COBOL 2 COBOL Object MicroFocus Fujitsu PacBase
FORTRAN language
LISP language, PROLOG
UML language
SQL language

MODELIZATION

Object Design Patterns,
Python language, Web Scraping, Scrapy, Selenium, BeautifulSoup
BigData Hadoop, Spark, Hive, Oozie, Zookeeper, Pig, Flume,
Neo4J, Redis
Elasticsearch, Kibana, Logstash
DataScience R language

DATABASES or INFORMATION SYSTEM

Database Oracle, Mysql, Sybase, Ingres, Universe, Informix, Access

FRAMEWORKS

BPM: jBPM, Camunda
ETL: Talend, Datastage
ERP: Odoo
CMS: Drupal
CRM: SugarCRM
E-Commerce: Prestashop, Magento
BI Lumira Business Objects

DEVOPS

Sonar code review,
Maven, Gradle, Continuous Integration Jenkins, Archiva, Confluence, Jira
JUnit test
Read more
Show more
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Contact Mohamed
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Similar classes
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Learn to add AI features to your applications using ChatGPT API. Taught by a Full-Stack Engineer who's integrated AI into real client projects.

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ChatGPT API Integration: Add AI chat, content generation, or text analysis to any web app
Prompt Engineering: Write effective prompts that give consistent, quality results
Production Implementation: Rate limiting, cost management, error handling, security
Real-World Deployment: Deploy AI features to AWS with proper monitoring and logging

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OpenAI API (ChatGPT-4)
React/Next.js frontend integration
Node.js/NestJS backend
Secure API key management
Cost optimization (don't waste money on API calls)

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Developers: Add AI to your portfolio (instant differentiation from other candidates)
Freelancers: Offer AI integration services
Students: Build an AI-powered projects that stands out
Entrepreneurs: Add AI features to your product without hiring an AI engineer

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AI chatbot for your website (customer support, FAQ assistant)
Content generation tool (blog posts, product descriptions, social media)
Text analysis features (summarization, sentiment, keyword extraction)
Custom AI assistant for your specific use case

Real Examples:

E-commerce: AI product description generator
Real estate: AI property listing writer
Marketing: AI social media content creator
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What's Included:
Working code you can deploy immediately
Cost management strategies (API calls can get expensive!)
Error handling and fallback strategies
Production deployment checklist

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Expertise & Teaching Areas:
✅ Programming & Software Development: Python, Java, C++
✅ Cybersecurity: Ethical hacking, data protection, network security
✅ Digital Literacy: ICT applications, online safety, cloud computing
✅ Data Science & AI: Data analysis, machine learning fundamentals
✅ Web Development: HTML, CSS, JavaScript

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🔹 Cambridge IGCSE & GCSE ICT & Computer Science – Teaching core and extended syllabi, focusing on programming logic, databases, and networking.
🔹 Cambridge A-Levels & O-Levels Computer Science – Preparing students for advanced computing concepts, problem-solving, and algorithm development.
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📌 Developed interactive lesson plans integrating real-world applications of technology.
📌 Conducted coding boot camps and cybersecurity workshops to enhance practical learning.
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Objective: To understand AI without fear, to use it to simplify one's life and to know how to identify digital traps.

1: Demystifying AI (What exactly is it?)
AI is not a movie robot: Difference between fiction and reality.

How it works (simply): The image of the "giant library": AI has read billions of books and uses them to predict the continuation of a sentence or create an image.

Where is it already present? Spell checkers, Netflix/YouTube suggestions, GPS, and voice assistants (Siri/Alexa).

2: Using AI to make life easier
Conversing with AI (ChatGPT, Claude, Gemini):

Ask him to write an administrative email or a complex letter.

Summarize a long newspaper article or document.

Plan a travel itinerary or find recipe ideas with what's left in the fridge.

AI for creativity and memory:

Generate images to illustrate a birthday card (Midjourney, DALL-E).

Using AI to restore or colorize old family photos.

3: Learning to "talk" to AI (The Art of the Prompt)
The context method: Why "Give me a cake recipe" is less effective than "I am allergic to gluten and I am hosting 4 people, give me a simple chocolate cake recipe".

The expert's role: Learning to tell AI "Act like a travel guide" or "Act like an expert gardener".

4: Precautions and Critical Thinking (The Survival Guide)
"Hallucinations": Understand that AI can make false claims with complete certainty (never take medical or legal advice from AI without verification).

Privacy protection:

Never give sensitive data (social security number, passwords, bank details) to an AI.

Knowing that everything we write to the AI is potentially used to train it.

Spotting "Deepfakes":

How to recognize a doctored image or video (details on the hands, strange reflections, slightly metallic voice).

Verify the information: the golden rule of cross-referencing sources.

5: Ethics and Impacts (To go further)
Copyright: Who owns an image created by AI?

The environmental impact: The water and energy consumption of AI servers.

The future: Will AI replace us or assist us?
verified badge
Erik
This course is aimed at students and scientists who want to use artificial intelligence to support the writing of academic papers without losing control of the content or compromising academic integrity.

The goal is not to delegate writing to AI, but to learn how to consciously and systematically integrate it into the scientific workflow. Throughout the course, the path from initial idea to final text is explored step by step, always with a critical eye.

We begin with chat setup and formulating appropriate prompts. Good interaction with AI doesn't depend on "tricks," but on clear questions and a well-defined framework. We'll show you how to guide the dialogue to obtain useful and consistent results.

Next, we work on developing the text's structure. AI can help develop a solid and logical outline, which will serve as the basis for subsequent argumentation. We explain how to evaluate and adapt these proposals according to academic criteria.

A central section is dedicated to source research. We analyze how AI can support information retrieval and sorting, and why citation and reference verification is essential. Methods for detecting errors, inaccuracies, and nonexistent sources are presented.

The proper use of footnotes and comments is also addressed. It explains how to integrate references, clarifications, and annotations without sacrificing clarity or formal rigor.

The final phase of the course focuses on writing and revising the text. It demonstrates how to use AI to improve style, coherence, and linguistic precision, as well as to identify argumentative problems. The course concludes with the process of exporting and preparing the final document for submission.

The course is designed for academic work such as essays, coursework, and theses, and is suitable for students of any discipline. No prior technical knowledge is required, only a willingness to work thoughtfully and responsibly.
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Contact Mohamed