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Development of Autonomous AI Agents: AG-UI Frontend, ADK Agents and A2A Communications, MCP

From 75 C$ /h

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.

Location

Use Ctrl + wheel to zoom!

At student's location :

Around Montreal, 10, Canada

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

Intermediate

Advanced

Duration

60 minutes

The class is taught in

French

English

Skills

Python

Reviews

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

WebCraft 101: Forge Your Way in the World of Web Programming

58 C$ /h

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. 💡🌐🚀

Support or initiation course on programming

54 C$ /h

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.

Adam

4.81

(26)

Learn to code with method and logic
Whether it's to succeed in the NSI (Digital Sciences and Technology) specialization in high school, design personal projects, or prepare for higher scientific studies, mastering code relies on solid algorithmic thinking. I help students understand the structure of programming languages and the logic of data.

Subject areas and languages taught:

Algorithms & Logic: Designing data structures and solving problems.

Programming Languages: Python, C/C++, C# and Java.

Data Management: Analysis and SQL queries / databases.

Basic Web Development: HTML & CSS for creating structured pages.

The goal is to take the student from simply writing code to true autonomy in development.

Amin

4.81

(26)

Python is the most in-demand programming language in the world right now — and one of the easiest to learn with the right guidance.
Whether you've never written a line of code or you're a student who needs to pass a programming course, this is a practical, no-fluff introduction that gets you writing real code from session one.
What we can cover depending on your goals:

Python fundamentals: variables, loops, functions, data structures
- Object-oriented programming (OOP)
- Data manipulation with pandas and NumPy
- Introduction to machine learning with scikit-learn
- Database management with SQL
- C and Java upon request
- MATLAB and R available for engineering/science students

Why learn with me?
I'm not a student teaching on the side — I'm a professional engineer who uses Python daily for data analysis, modeling, and automation. I know exactly which concepts matter in the real world and which ones you can skip for now.
Sessions are 100% personalized: I adapt the pace, the examples, and the exercises to your background and your goal — whether that's passing your university exam, building a project, or landing a job.

Lucas

4.81

(26)

This course aims to provide a solid foundation of programming concepts and techniques, using popular programming languages such as C, C++ or Python. This course is suitable for people who are new to programming or who already have some basic knowledge and want to improve their skills. Whether you want to become a software developer, get into data analysis, or simply learn to code, this course will provide you with the essential knowledge and practical skills needed to succeed.

Python is an essa

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

2 — PROGRAMMING, ALGORITHMS, AND COMPUTER SCIENCE FOUNDATIONS
• 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

4- MATHEMATICAL FOUNDATIONS
• 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

6- UNSUPERVISED LEARNING
• 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

7- MODEL EVALUATION AND IMPROVEMENT
• 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

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.

Python is an essa

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