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Computer science, Algorithms and Programming Course

From 19 C$ /h

I am a computer engineer and I offer courses to students who wish to consolidate their theoretical knowledge and practical skills in computer science. The courses are adaptable according to your needs and the field of computer science that you wish to consolidate the most. However, I suggest you choose one of the following specialty areas that I master: Algorithmics, Programming (C / C ++, Java, C #, Python), Web (HTML, CSS, Javascript, Ajax, XML, etc.), Data structure .

As a computer engineer, I offer tailored courses to help students strengthen their understanding and practical skills in computer science. My specialties include algorithmics, programming languages such as C/C++, Java, C#, and Python, and web development using technologies like HTML, CSS, JavaScript, Ajax, and XML. I am happy to work with you to focus on the specific areas of computer science that you would like to improve upon.

Location

Online from Canada

About Me

I am passionate about computer science. Since the age of 14, I have been developing algorithms and programs. I am a computer engineer and hold a master's degree in information technology.

I'm passionate about the world of computer science; I started developing algorithms and writing code when I was 14. I'm a computer engineer and I also have a master's degree specializing in machine learning.

Education

Three years at Ensias, training as a computer engineer specializing in BI.
Two years of master's studies at the University of Montreal, specializing in machine learning algorithms.

Three years at Ensias, computer engineering training specializing in BI.
Two years of master's studies at the University of Montreal, specializing in machine learning algorithms

Experience / Qualifications

With 6 years of experience as a developer, I have worked on several IT projects and have used several programming languages such as C#, Python, Javascript, etc.

With 6 years of experience as a developer, I have worked on several IT projects and have used several programming languages such as C#, Python, Javascript, etc.

Age

Teenagers (13-17 years old)

Adults (18-64 years old)

Seniors (65+ years old)

Student level

Beginner

Intermediate

Advanced

Duration

60 minutes

120 minutes

The class is taught in

English

French

Arabic

Skills

Computer science

Algorithms

Computer programming

Reviews

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

Advanced web application development / Web application development

43 C$ /h

This is an advanced course: learn how to develop web applications from zero to hero. A simple application development which will take around 10 hours of course depending on your level in programming.

- front end: reactjs
- back end: c# with dotnet or python with django rest framework
- database: mongodb, mysql or postgres
- publishing using AWS.
- using git/github for versioning

prerequisite:
- sql database data manipulation
- algorithms and programming

In this course, you will learn how to make a modern web application. You will be able to develop your skills for creating a database, and to connect it to the web application. Publishing the solution will not scare and you will be able to see how you can put your website online. You will create your own domain so that everyone can access your website. You will be able to use github and git in order to keep track of your application maintenance and development. Testing your application for better quality.

*text me to learn more about the course

4.85

(13)

Hello, I am a doctoral student in electrical engineering and associate professor in engineering sciences, experienced in the field of electrical engineering, I offer support courses in the subjects of engineering sciences (Electronics, automatics, electrical engineering, automation, programming).

Digital electronics
Analog electronic
electromagnetism (propagation of high frequency waves)
Automatic (continuous, sampled)
electrical engineering (transformers, electrical machines, switching power supply)
C / c ++ programming, Assembler, ARM, STM32
renewable energy (wind, PV)
engineering Sciences
RDM
Python,VHDL
PIC Microprocessor and Microcontroller
Signal processing and data acquisition
Engineering Sciences

These courses allow the student to get up to speed and regain confidence in all scientific subjects, just as they prepare him effectively for the Baccalaureate, the Preparatory Classes or various examinations of the engineering classes.

COURSE OBJECTIVES AND PEDAGOGICAL APPROACH

Resumption and deepening of fundamental concepts through exercises with course reminders.

Put the student in a situation of questioning and research.

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.

Specific preparation for higher education requirements (in-depth content, increase in work capacity, enrichment of scientific background)

This educational approach is effective since it has often led me to interesting results with my students.

Associate professor provides support courses in electrical engineering

Robert

4.85

(13)

As a Franco-Belgian management teacher, I give Excel lessons with passion!
Whether remotely or face-to-face, I offer many examples and exercises to accompany you.
I travel without problem throughout the region of Brussels and its surroundings, for lessons of at least 2 hours. For France, courses are only given remotely.

Here are some key words that will be covered in my classes:
Scenario analysis, Year, Rounding, Today, Bdnb, Bdnbval, Bdsum, Search, Column, Copy/paste in values, Copy/paste with transposition, Consolidation, Date, Datedif, Determat, Dollar, Right, Righterg, Equiv, Esterror, Estna, Frequency, Filter (simple and advanced), Format of cells, Left, Large.Value, Printing of documents, Index, Indirect, Inversemat, Day, Weekday, Line, Matrix, Max, Maxa, Max.Si, Min , Mina, Mina.If, Formatting of cells and ranges, Month, Average, Average.If, Nb, Nb.If, Nbval, Naming of cells and ranges, No, Small.value, Product, Productmat, Protection of cells, Lookup (Lookup), Lookupv (VLookup), Lookuph (HLookup), If (If), If.Not.Disp, If.Conditions, Iferror, Sum, Sumproduct, Sum.If, Sum.If.Set, Substitute , Pivot tables, Sorting, Cell locking

Do not hesitate to contact me to organize your lessons according to your needs and availability. Together, we will develop your Excel skills in an efficient and personalized way.

Adam

4.85

(13)

doctoral student in engineering sciences provides support courses in analog and digital electronics at any DEUG level and engineering schools. having scientific and technical knowledge, three years of experience in the field of teaching, pedagogy and a sense of listening and analysis, I am able to help pupils and students and train them in the chapters of which they are having difficulty. for more info please contact me

Gaëlle

4.85

(13)

While adults are still debating whether kids should use AI, they are already using it.
The question isn't "should they?" it's "how do we do it intelligently?"

In this course, your child will discover:
✓ What AI actually is: not magic, not mystery. How machines think, what they can do, what they can't
✓ How ChatGPT really works: not just "ask a question and get an answer," but why it responds that way, where it fails, when to trust it
✓ What LLMs are (Large Language Models): in language they understand, not tech jargon
✓ Create with AI: custom avatars, interactive stories, real projects using real tools
✓ Think critically about AI: Bias, privacy, creativity. What does AI do better than humans? What can't it do?
✓ Real-world applications: How AI transforms medicine, education, art, gaming, everyday life

Why this is different:
Most AI courses for kids teach "here's the tool, use it." I teach how to think about AI.
Your child will learn to see AI not as black magic or a solution to everything, but as a powerful tool with real limits.
And, more importantly: that they can control how they use it.

What they take home:
Real projects they created (custom avatar, interactive app, analysis of a real AI case study). A genuine understanding of how it works. And the ability to use AI responsibly and creatively.

Format: Online | 60–90 min sessions | Flexible, adapted to their age and pace

For curious kids asking "How does ChatGPT actually know things?"

Reza

4.85

(13)

You will learn Systematic Reasoning & Logical Thinking which is a requirement for entering Computer Science program in many universities.
The book “Delftse Foundations of Computation” especially its second chapter will be the main source of our lesson, but other more in-depth books will be also covered if you want to improve even further on logical thinking.
The topics in our lesson include:
• Propositional Logic: Logical operators; Precedence rules; Logical equivalence; Implications in English; Exclusive or; Universal operators; Classifying propositions
• Boolean Algebra: Substitution laws
• Logic Circuits: Logic gates; Combining gates to create circuits; From circuits to propositions; Disjunctive Normal Form; Binary addition.
• Predicate Logic: Predicates; Quantifiers; Tarski’s world and formal structures;
• Deduction: Valid arguments and proofs; Proofs in predicate logic

If you have any additional questions before starting a class, please feel free to ask me. I am here to assist! :)

Florian

4.85

(13)

Experienced and patient teacher of logic for computer science.

I have taught logic, formal languages and automata theory to undergraduates for six years. My tutoring is adapted to the student's level and goals. Whether you need to learn logic for your studies, or you would simply like to know more about the subject, I will be more than happy to help you improve your understanding and skills.

Logic
The sciences presuppose a certain standard of rationality. An ability to distinguish between correct reasoning and claims that do not follow from the assumptions. In this class we study the basic principles of logic and apply mathematical techniques to the study thereof.
Topics include:
Propositional and Predicate Logic
Syntax and semantics
Natural deduction
Semantic tableaux
Correctness and soundness
Completeness

Formal languages and automata
A formal language is an abstraction of general characteristics of programming languages. Such a languages consists of a set of symbols together with some rules to determine whether a string made up out of those symbols is a member of the language.

Topics include:
Regular languages, context-free languages
Finite automata, pushdown automata, Turing machines
Regular expressions
Regular grammar, context-sensitive grammar
Pumping lemmas for regular and context-free languages

Baia

4.85

(13)

I teach Python specifically for finance and data applications - the kind used in economics, business analytics, and quantitative programs. This isn't a general "learn to code" course; it's built around real financial data, benchmarking, and the workflows you'll actually use in coursework or early career work.

Topics include:
Python fundamentals through a finance lens (data structures, functions, control flow).
Working with financial data and datasets.
Performance benchmarking and writing efficient code.
Applying concepts from Hilpisch's Python for Finance.

Yakine

4.85

(13)

Experienced Teacher – Mathematics, Statistics & Computer Science

With more than 10 years of experience, I support pupils and students from middle school to university, as well as candidates for competitive exams (BCE, ECRICOME, TAGE MAGE, IAE MESSAGE, etc.).

Courses offered:

Mathematics (academic support, in-depth study, exam/competition preparation)
Applied Statistics & Probability
Computer science: Python, SQL, VBA, Data Mining
Methodological coaching (organization, efficiency, self-confidence)

Method: progressive and personalized approach, based on a diagnostic assessment, targeted exercises and regular monitoring to ensure lasting progress.

In-person or online teaching – individual or small group lessons.

Strengths:
More than 10 years of experience
Concrete results (students admitted to the best schools)
Clear, structured and motivating teaching

Prices adapted according to the level and the package chosen.
First free exchange to assess your needs.

Achieve your goals with quality support

Raouf

4.85

(13)

Objective: To understand AI without fear, to use it to simplify one's life, to know how to identify digital traps, and to use Word, Excel, etc. without difficulty.

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?

Laroussi

4.85

(13)

*Goal: To understand artificial intelligence without fear, use it to simplify life, and uncover digital traps.*

### **1: Demystifying Artificial Intelligence (What Exactly Is It?)**

* **Artificial intelligence is not a "movie robot":** The fundamental difference between science fiction and practical reality.
* **How it works (simply):** Imagine a "giant library"; AI has read billions of books and uses them to predict the completion of a sentence or create a new image.
* **Where do we use it now?** The spell checker, Netflix and YouTube suggestions, GPS navigation, and voice assistants such as Siri and Alexa.

---

### **2: Using artificial intelligence to make your life easier**

* **Interacting with artificial intelligence (ChatGPT, Cloud, Gemini):**
* Writing formal emails or complex letters.
* Summarizing long articles or huge documents.
* Plan travel itineraries or create food recipes from ingredients available in the refrigerator.

* **Creativity and Memory:**
* Create creative images for greeting cards (via Midjourney or DALL-E).
Restoring and coloring old family photos.

3: The art of speaking to the machine (the Prompt skill)**

* **Context Style:** Why is the phrase "Give me a cake recipe" less effective than "I have a gluten allergy and I'm having 4 people, give me a simple chocolate cake recipe".
* **Role-taking:** Learn to ask the artificial intelligence to "speak like a tourism expert" or "answer me like a specialized agricultural engineer."

4: Precautions and Critical Thinking (A Survival Guide)**

Digital hallucinations:** Understand that artificial intelligence may confidently present false information (never rely on it for medical or legal advice without verification).
Privacy protection
Do not share sensitive data (ID numbers, passwords, bank details).
Be aware that everything you write may be used to train systems in the future.

Detecting deepfakes:**
How to identify fake photos or videos (examine hand details, strange reflections, or metallic sound).
* The golden rule: Verify by cross-referencing different sources.

5: Ethics and Impact (A Future Perspective)**

Copyright: Who owns the image created by artificial intelligence?
Environmental impact: Water and energy consumption in large data centers.
The future: Will artificial intelligence replace us or will it be an assistant to us?

Additional tip:** Since you are targeting the Gulf region, it is preferable to use terms such as "Digital Transformation" and "Innovation" at the beginning of your presentation, as they are very catchy words for decision-makers there.

Farouk

4.85

(13)

These courses are part of a structured and progressive training in Object-Oriented Programming (OOP) with JavaScript, designed for beginner or intermediate developers who want to understand in depth how the language works, write clearer, more maintainable code and prepare themselves calmly for modern frameworks like React ⚛️.

Object-Oriented Programming is often perceived as complex or abstract.

My goal is simple: to make it logical, concrete, and immediately applicable.

🎯 Training Objectives

Upon completion of this training, you will be able to:

Understanding what Object-Oriented Programming really is (and when to use it)
Create and manipulate objects in JavaScript in a clean and efficient way
Use ES6 classes, constructors, and methods with confidence
Mastering this, the prototype, and the instantiation logic
Apply encapsulation, inheritance, and polymorphism without confusion
Avoiding common mistakes made by OOP beginners
Structure your JavaScript code like a professional developer

📖 Training Plan – Object-Oriented Programming in JavaScript
1. Introduction to Object-Oriented Programming 🧠
Understanding the concept, objectives and benefits of OOP.
2. Procedural Programming vs. OOP
Why unstructured code quickly becomes unmanageable.
3. Objects in JavaScript
Properties, methods and representation of the real world.
4. The keyword this
Understanding the execution context (often poorly understood).
5. Limitations of simple objects
Why duplicating code is a bad idea.
6. Constructive functions
Create multiple objects from the same model.
7. The keyword new
What it's actually doing under the hood.
8. The prototype
Sharing methods and memory optimization.
9. ES6 Classes
Modern syntax and best practices.
10. The builder
Proper initialization of objects.
11. Data Encapsulation
Protect the internal state of objects.
12. Inheritance between classes
Reusing code intelligently.
13. The keyword super
Communication between parent and child in the classroom.
14. Polymorphism
The same behavior, several forms.
15. Composition vs. Inheritance
Choosing the right architecture.
16. Best practices in OOP
Write readable, scalable, and maintainable code.
17. Common mistakes made by beginners
Pitfalls to absolutely avoid.
18. Guided practical exercise
Creation of a concrete class (product, user, etc.).
19. Assessment Quiz (Multiple Choice Questions)
To validate the actual understanding of the concepts.

🛠️ Teaching method: Understand before writing

This training program is based on a progressive and pragmatic approach:
Clear and illustrated explanations
Concrete examples from real projects
Simple but effective exercises
Constant questioning to avoid rote learning
Adaptation to the learner's level and pace
Here, we don't "recite OOP" — we understand it.

🚀 Learner's result

At the end of the training, you will not only know how to write a JavaScript class.
You will know:

1- Why does it exist?
2- When to use it
3- and when not to use it

You will leave with:
a solid understanding of OOP
a cleaner and more professional code
an ideal foundation for learning React, Node.js or any other modern framework

Ali

4.85

(13)

I offer one-to-one Machine Learning and AI tuition for university students, postgraduates, working professionals, and serious self-learners. Lessons are available online or in person around Birmingham.
What I cover:

Python for data science and ML (NumPy, Pandas, Scikit-learn)
Deep learning with TensorFlow and Keras
Core ML concepts: regression, classification, clustering, neural networks, CNNs
Computer vision and image classification (my published research area)
University coursework support, dissertation help, project guidance
Help with Kaggle competitions and personal portfolio projects

How I teach:
I focus on understanding, not memorisation. We work through real datasets and real problems — not toy examples — so you can actually apply what you learn. I'll help you build a model from scratch, debug it when it doesn't work, and explain the maths behind why it does or doesn't perform well. For university students, I can also help with assignments, dissertations, and final-year projects.
Whether you're just starting out, stuck on a coursework project, or trying to break into ML professionally, I can meet you wherever you are and help you move forward.

Ammar

4.85

(13)

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- 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

3- 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

4- 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

5- 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

6- 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

7- 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

8- 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

9- 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

10- 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

11- 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

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 free 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.

Varnika

4.85

(13)

Whether you're just starting your computer science journey or preparing for university exams, I offer personalized tutoring tailored to your goals and learning style. I can also make the journey intuitive and interesting by teaching some design softwares as Canva and Adobe InDesign.

I hold a Bachelor's degree in Computer Science Engineering and have experience applying technical problem-solving in international organizations alongside my work in technology, accessibility, and data analysis.

I can help with:
Microsoft Excel, Powerpoint and Word
Canva, and Creative Designing using Adobe
Programming fundamentals
Python
SQL and databases
Data structures and algorithms
Object-oriented programming
Software engineering concepts
Computer networks
Information security fundamentals
Operating systems
Exam preparation
Assignments and projects
Problem-solving and coding practice

My teaching approach focuses on building a strong understanding of concepts rather than memorization. I adapt every lesson to the student's pace, using practical examples and interactive exercises to make complex topics easier to understand. We learn together by solving creative problems, and I am available to support students at any moment of their journey.

Whether you're in school, university, or simply interested in learning programming, I'll help you develop confidence and the analytical thinking skills needed to succeed.

Abdelrahman

4.85

(13)

This class covers university-level Computer Science and Engineering coursework across a wide range of topics, including Operating Systems, Databases, Software Engineering, Computer Organization, Data Structures & Algorithms, Discrete Mathematics, Computer Networks, and other core CS/CE subjects. Sessions are built around your specific course material, textbook, or exam syllabus, working through concepts, past exam questions, assignments, or project support depending on what you need. The focus is on connecting theory to how it's actually applied, so ideas are easier to retain and use — not just memorize for a test. Whether you need help catching up on a specific topic, preparing for an exam, or working through a course project, sessions are tailored to your goals.