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Since November 2021
Instructor since November 2021
Translated by GoogleSee original
WebCraft 101: Forge Your Way in the World of Web Programming
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From 37 € /h
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Are you ready to dive into the exciting world of building modern web applications, even if you've never heard of JSON, HTTP requests, or all that seemingly cryptic terminology? Then you are in the right place! Our course is specially designed for beginners, and you will be guided step by step through the exciting world of web programming.

Imagine creating your own web application, whether it's a personal project or a revolutionary idea you want to share with the world. You'll discover the "Frontend", where the visual magic happens, and the "Backend", the brains of the application that ensures everything works as expected. We'll explain databases, those magic boxes that store information, and show you how to make them interact with your application.

You will also be introduced to the art of creating a solid infrastructure to host your application on the Internet, allowing users to join it from anywhere, at any time. And don't worry, we'll teach you everything about HTTPS requests, these secure communication channels, and load balancers, which guarantee a smooth user experience.

Prepare for an exciting journey into the world of web programming, even starting from scratch. Join us to master the essential skills that will help you bring your ideas to life on the modern web. 💡🌐🚀
Extra information
Courses via Teams
Location
location type icon
Online from Canada
About Me
I have been working in IT for 25 years, Oracle and Google Cloud specialist, Python and Node developer (Java in the past). Computer science is a passion since I was 15 years old, and I'm 50 years old. Married and father of a daughter.
I like to transmit my passion.
Today I am specialized in data engineering and I have been practicing Machine/Deep Learning (Python) for 5 years. I developed several complete software used by companies: an ERP for the horticulture sector, a SSO solution for an Oracle product with OpenID, SAML, Kerberos support.
Education
University degree in Industrial Computing, obtained in 1993 in Lyon (France), completed by a year in telecom network at the University of Nice in 1994. Various trainings throughout my professional years : Management, Security, Oracle trainings, Machine Learning, Deep Learning.
Experience / Qualifications
Oracle: 25 years old, certified professional architect on Oracle Cloud
Google Cloud: Certified Data Engineer
Machine Learning/Deep Learning: 5 years
Java: 20 years
Python: 11 years
Node: 10 years
Age
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Duration
60 minutes
The class is taught in
French
English
Reviews
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
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.
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Dive into the exciting world of generative AI! This hands-on course will allow you to create your own applications using language models and explore the endless possibilities this technology offers. Using Google Cloud and Python, you will learn to:

Use language models, with a focus on Google Gemini during lessons
Generate creative text, translate languages and answer complex questions
Deploy your applications
Understand the ethical issues linked to generative AI
Read more
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• Transformer architecture, tokens, embeddings, attention mechanisms, prompt engineering, Retrieval-Augmented Generation (RAG), and model evaluation
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-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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Personalized Teaching Approach: Each course is tailored to your skill level and individual goals.

Practical Experience: Learn by doing with real-world projects that build your understanding and skills.

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As a Python expert, I have a passion for teaching and sharing my knowledge. My goal is to guide you effectively in your learning journey.

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Python fundamentals through a finance lens (data structures, functions, control flow).
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Applying concepts from Hilpisch's Python for Finance.
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Variables

Loops

Functions

Data structures

Practical projects for implementation

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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

Create your own projects in Python and gain independence
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I support participants in a pedagogical and progressive manner, adapting to their level and objectives (university courses, training, practical work, exams, projects).
The goal is to understand, practice and gain autonomy through clear explanations and concrete examples.
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You will learn to design practical, creative proposals, developing the necessary skills to apply them in the classroom or in competitive examination processes.
verified badge
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
• Lists, tuples, dictionaries, sets, comprehensions, debugging, and writing clear, reusable, well-structured code
• Jupyter Notebook, Anaconda, Visual Studio Code, virtual environments, and package management

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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

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• 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
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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

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• 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

11- TOOLS AND LIBRARIES
• 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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B- PERSONALIZED TUTORING: LEARNING HOW TO REASON
Machine learning and artificial intelligence become much more accessible when mathematics, algorithms, Python code, data, and real-world applications are clearly connected.

My lessons help you move beyond simply copying code or using models as “black boxes.” You will learn how to define the problem correctly, prepare the data, select an appropriate algorithm, understand how it works, train and evaluate the model, diagnose errors, improve performance, and interpret results rigorously and responsibly.

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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This cohort is designed for young people who want to learn in an affordable, flexible, and enjoyable way without having to dedicate a huge amount of time each week or even just extra support.

This beginner-friendly course introduces students to the world of computers and computer science through simple explanation.

Students will learn how computers work, including hardware, software, memory, storage, data, and how a computer processes information. They will then explore how applications are used to create and organize information, with practical experience using tools such as Microsoft Word, PowerPoint, and Excel.

As the course progresses, students will be introduced to important computer science concepts including binary numbers, algorithms, programming, databases, networks, the Internet, and cybersecurity.

By the end of the course, students will have a good foundation in computer science and improved digital skills.
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