Web and Mobile Development: Learn how to create modern applications with Flutter, Firebase, and web technologies

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From 29 € /h

Do you want to learn to program or develop your own applications? This course is designed for beginners, students, and professionals who want to acquire practical skills in software development.

During the training, you will learn to:

- Understand the basics of programming.
- Develop websites using HTML, CSS and JavaScript.
- Designing mobile applications with Flutter.
- Use Firebase (authentication, Firestore database and storage).
- Create modern and professional interfaces.
- Deploy and test your applications.
- Apply the best practices used in the professional world.

The lessons are tailored to each student's level and alternate between theoretical explanations, practical exercises, and the completion of concrete projects. My goal is to enable you to acquire skills directly applicable to your studies, work, or personal projects.

Extra information

A laptop is recommended. The necessary software will be installed together during the first few sessions if needed.

Location

Online from Cameroon

About Me

Bonjour et bienvenue !

Je suis ingénieur des travaux en télécommunications, passionné par les technologies de l'information, le développement logiciel et les réseaux informatiques. J'aime transmettre mes connaissances de manière simple, pratique et progressive afin que chaque élève puisse évoluer à son rythme.

Ma méthode d'enseignement est basée sur la pratique. Je privilégie des exercices concrets, des projets réels et un accompagnement personnalisé pour permettre à mes élèves de comprendre les concepts et de devenir autonomes. Je prends le temps d'expliquer chaque notion jusqu'à ce qu'elle soit parfaitement assimilée.

J'enseigne notamment le développement web (HTML, CSS, JavaScript), le développement d'applications mobiles avec Flutter, Firebase, les réseaux informatiques, Linux, ainsi que les bases de la programmation et de l'informatique.

J'accompagne aussi bien les débutants que les étudiants, les professionnels ou toute personne souhaitant acquérir de nouvelles compétences, préparer un examen, réaliser un projet ou se reconvertir dans le domaine de l'informatique.

Mon objectif est que chaque élève termine ses cours avec des compétences solides, de la confiance en lui et la capacité de mettre en pratique ce qu'il a appris.

Education

École Nationale Supérieure des Postes, des Télécommunications et des TIC (SUP'PTIC), Cameroun

Diplôme d'Ingénieur des Travaux en Télécommunications.

Formation spécialisée en réseaux informatiques, télécommunications, systèmes d'information, programmation, administration des systèmes et technologies numériques.

Experience / Qualifications

- Ingénieur des Travaux en Télécommunications.
- Développeur Web et Mobile (Flutter, Firebase, HTML, CSS, JavaScript).
- Conception et déploiement de réseaux informatiques (LAN, Wi-Fi, TCP/IP).
- Administration de systèmes Linux et Windows.
- Développement d'applications connectées à des bases de données.
- Accompagnement d'étudiants et de débutants dans l'apprentissage de l'informatique, de la programmation et des réseaux.
- Expérience dans la réalisation de projets informatiques complets, de la conception au déploiement.

Age

Children (7-12 years old)

Teenagers (13-17 years old)

Adults (18-64 years old)

Student level

Beginner

Intermediate

Duration

60 minutes

90 minutes

The class is taught in

English

French

Skills

Computer programming

Web development

Mobile development

Availability of a typical week

(GMT -04:00)

New York

Online via webcam

Mon

Tue

Wed

Thu

Fri

Sat

Sun

00-04

04-08

08-12

12-16

16-20

20-24

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engineering Sciences
RDM
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PIC Microprocessor and Microcontroller
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Resumption and deepening of fundamental concepts through exercises with course reminders.

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Respond to individual issues and questions

Exercise training in order to achieve real mastery of the content.

Learn to build theoretical reasoning from observable facts or hypotheses.

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With over 12 years of tutoring experience, I have successfully guided more than 50 students worldwide to achieve distinction in various fields. Consistent results are my priority, and I strive for excellence in all aspects of my teaching.

My lessons are customized to meet each student's unique needs and are designed to be engaging and insightful.

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- High school studies and diploma programs.
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I prioritize a dynamic and interactive learning experience, with one-on-one sessions tailored to your individual requirements. Lessons will be conducted via webcam, enabling you to connect from anywhere.

I have a highly flexible schedule and can adapt to accommodate your needs.
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Master Python with Personalized Courses

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Why Choose My Courses?

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.

Ongoing Support: Get unlimited email support for any questions you have between sessions.

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.

Book Your First Lesson:

Start your journey to Python mastery now by booking your first lesson. Whether you aspire to enter the development field or hone your existing skills, these courses are designed for you.

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I really happy to teach people who will really want to part of my lesson.

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A very warm welcome my students on apprentus.com

Wish you all the best.

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Introduction to the Web: Understanding the Domain Name /
Accommodation.
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Python Programming Fundamentals to Advanced Concepts
HTML for Website Structure
CSS for Modern Web Design & Styling
JavaScript for Interactive Web Development
Real Projects and Practical Assignments
Debugging and Problem-Solving Techniques
Frontend Development Basics
Teaching Style:
Step-by-step guidance
Beginner-friendly explanations
Practical and project-based learning
Live coding sessions
Personalized support and doubt clearing
Easy learning methods with real examples

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School & college students
Beginners in coding
Future web developers
Anyone wanting practical programming skills

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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
• 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.

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