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Since June 2020
Instructor since June 2020
Translated by GoogleSee original
Programming Languages / Databases / Frameworks / Devops
course price icon
From 52 £ /h
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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
Extra information
Good internet connection> 2 MB
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 -04:00)
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
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.
Read more
Show more
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This course is for anyone who wants to:

✅ Learn Python from the beginning
✅ Strengthen their programming skills

📚 On the program:

Variables

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Practical projects for implementation

💡 How does the course work?

Clear explanations to understand the programming logic

Targeted exercises adapted to your level

Concrete projects to create your own applications

🎯 My goal:

Helping you understand the logic behind the code

Progress at your own pace

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A- TOPICS YOU CAN EXPLORE AND MASTER:
1- PYTHON FOUNDATIONS
• Variables, data types, operators, conditional structures, loops, functions, modules, files, exceptions, and object-oriented programming
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• Algorithmic thinking, problem decomposition, pseudocode, flowcharts, procedural programming, object-oriented programming, recursion, and modular program design
• Fundamental data structures including arrays, lists, stacks, queues, dictionaries/hash tables, sets, trees, graphs, and their appropriate use
• Searching, sorting, traversal, algorithm efficiency, computational complexity, Big-O notation, debugging, testing, code organization, and problem-solving strategies
• Practical programming exercises ranging from beginner problems to university-level algorithmic and computational challenges

3- DATA PREPARATION AND EXPLORATION
• NumPy and pandas for importing, cleaning, transforming, filtering, grouping, reshaping, and merging data
• Missing values, duplicates, outliers, inconsistent formats, data leakage, and data-quality validation
• Exploratory data analysis using descriptive statistics, Matplotlib, Seaborn, and graphical interpretation

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• Linear algebra, vectors, matrices, derivatives, optimization, probability, and statistics
• Loss functions, gradients, distance measures, regularization, likelihood, and model complexity
• Mathematical concepts are explained according to the learner’s level and the requirements of the selected algorithms

5- SUPERVISED MACHINE LEARNING
• Linear and polynomial regression, logistic regression, and regularized models
• k-nearest neighbours, decision trees, random forests, gradient boosting, support vector machines, and Naive Bayes classifiers
• Classification, regression, model assumptions, decision boundaries, feature importance, and interpretation of results

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• Clustering using k-means, hierarchical clustering, and density-based methods
• Principal component analysis, dimensionality reduction, anomaly detection, and pattern or structure discovery
• Method selection, evaluation of data structure, and interpretation of results without predefined labels

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• Training, validation, and test sets; cross-validation; hyperparameter optimization
• Accuracy, precision, recall, specificity, F1 score, ROC–AUC, confusion matrices, MAE, MSE, RMSE, and R2
• Underfitting, overfitting, bias–variance trade-off, class imbalance, feature engineering, feature selection, scaling, and regularization

8- DEEP LEARNING
• Neural-network foundations, activation functions, forward propagation, backpropagation, and gradient descent
• Multilayer perceptrons, convolutional neural networks, recurrent neural networks, and Transformer foundations
• TensorFlow, Keras, or PyTorch depending on the learner’s project and working environment

9- ARTIFICIAL INTELLIGENCE APPLICATIONS
• Natural language processing, text classification, embeddings, sentiment analysis, and foundations of language models
• Computer vision, image classification, fundamental principles of object detection, and image preprocessing
• Recommendation systems, forecasting, anomaly detection, intelligent automation, and decision-support applications

10- GENERATIVE AI AND LARGE LANGUAGE MODELS
• Transformer architecture, tokens, embeddings, attention mechanisms, prompt engineering, Retrieval-Augmented Generation (RAG), and model evaluation
• Use of artificial-intelligence APIs, vector databases, document-retrieval systems, and structured AI-enabled workflows when relevant
• Reliability, hallucinations, bias, privacy, responsible use, and appropriate human validation

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• Python, NumPy, pandas, Matplotlib, Seaborn, scikit-learn, SciPy, Statsmodels, TensorFlow, Keras, and PyTorch
• Jupyter Notebook, Anaconda, Visual Studio Code, Git, GitHub, SQL, Excel, and Power BI when useful to the project
• Additional libraries may be introduced depending on the selected specialization and dataset

12- PROJECTS, RESEARCH, AND INTERVIEW PREPARATION
• Complete projects covering data preparation, model development, evaluation, interpretation, and presentation of results
• University assignments, dissertations, theses, research projects, portfolio projects, technical interviews, and professional applications
• Code review, debugging, documentation, reproducibility, model comparison, and communication of results

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Machine learning and artificial intelligence become much more accessible when mathematics, algorithms, Python code, data, and real-world applications are clearly connected.

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Each lesson is personalized according to your current level, mathematical background, programming experience, dataset, university work, research project, interview preparation, or professional objective. We begin by identifying your existing knowledge, software environment, expected outcomes, and main conceptual or technical difficulties. We then establish a structured learning plan.

The first lesson combines a discussion of your background, objectives, and tutoring needs; an initial assessment of your current knowledge; personalized planning and organization of future sessions; and a short trial lesson to determine the most effective learning approach.

A typical session may include conceptual explanation, development of mathematical intuition, live coding, guided implementation, model evaluation, technical problem solving, and a concise summary of the next steps.

You may work with your own dataset, university assignment, research project, or professional problem, provided that confidential information is handled appropriately. I can also provide structured examples and datasets suited to your level.

My goal is not simply to help you run an algorithm. It is to help you understand why it is appropriate, how it learns from data, how to evaluate it correctly, why it may fail, and how to build a reliable, interpretable, and scientifically rigorous solution.
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What if you could think like a hacker, so you can stop one? Let's learn cybersecurity the real way.

I'm a cybersecurity engineer with 3 years of experience and 150+ penetration tests and vulnerability assessments for government, financial, and telecom organizations. I hold CRTP, eWPT, and eJPT certifications, and I'll teach you what actually works in the field. No boring theory dumps, just live, hands-on learning.

You'll learn:

How attackers think: the hacking process from recon to exploitation
Common web attacks like SQL injection and XSS, and how to stop them
Passwords, phishing, and social engineering: why people are the weakest link
Vulnerability scanning and ethical hacking basics with Nmap, Burp Suite, and Nessus
How defenders work: firewalls, SIEM, EDR, and incident response
How to protect yourself, your team, and your business

Perfect for: beginners, career changers, students, developers, IT staff, and anyone curious about ethical hacking or preparing for Security+, CEH, or eJPT.

You'll walk away with a clear understanding of real threats, the skills to spot and fix common vulnerabilities, and a solid foundation for a cybersecurity career.

No experience needed, just curiosity and a computer. Book your first session and let's dive in!
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This course is designed for students and adolescents who want to build a strong beginner-friendly foundation in Computer Science.
Whether you're completely new to Computer Science or need help with a specific programming subject, the course can be customized to match your needs.
Need help with C++ or programming? You can provide me with your syllabus, course outline, or the topics you're studying, and I'll tailor the lessons around what you need to learn.
Learning with friends? Group lessons are also available, allowing you to learn together while following the same customized course.
Good-fit Instructor Guarantee
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Contact Mohamed