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Since September 2016
Instructor since September 2016
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The basics of programming in Java and Python and C.
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From 64 C$ /h
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Learning to program neatly and effectively can be difficult and time consuming without adequate explanations. That's why I offer programming learning courses for the following languages:

-Python (Easy to take in hand)
-Java (more intermediate level to start)
-C (quite complicated without any knowledge in programming)

The purpose of the various courses will be to help future programmers to take the programming tool into their own hands but also the appropriate programming techniques.

At the end of these courses, the programmer will be able to create a program of the size that he wishes without any restriction except for his imagination. He will also be able to find the necessary information where it is needed on the web.
Extra information
Bring your computer if you have any favorite software to program with.
Location
location type icon
Online from United Kingdom
About Me
I am a private tutor from Belgium, having moved to the UK in 2018. For the past nine years, I have helped many students to overcome their difficulties in lots of different subjects such as Maths, Computing, Physics but also in English and Science. I began private tutoring during my time at university due to my passion for helping others which has helped me improve my teaching methods and I am now completing my English and Maths GCSE here in the UK in order to begin a PGCE next September 2019.

My Qualifications:
- Masters Degree in IT (which helped me to deepen my knowledge of Mathematical logistics and also improved my way of explaining different problems.)

I want to start doing private tutoring here in the UK to share my knowledge with students as well as helping them to become confident with themselves and their capabilities. In my opinion, showing a student that he or she is able to solve a problem or understand something that he or she thought out of reach can really improve their confidence and this is often the key to them achieving a good grade at school. Often, the lack of understanding from the previous years can block a student in his comprehension of a lesson and make him or her feel demotivated by the subject. I believe I can manage to fix this by providing them with the right support and most importantly, going back to gaps in their knowledge that occurred in previous years. It is necessary for them to have a good foundation in their subject to help them move forward and be able to grasp more complex ideas.

During my lessons, I will work at the pace of the students as it can help them not to be under pressure. I can give them all the support they need with an ear to listen and lots of original exercises.
Education
My Qualifications:
- Masters Degree in IT (which helped me to deepen my knowledge of Mathematical logistics and also improved my way of explaining different problems.)

I want to start doing private tutoring here in the UK to share my knowledge with students as well as helping them to become confident with themselves and their capabilities. In my opinion, showing a student that he or she is able to solve a problem or understand something that he or she thought out of reach can really improve their confidence and this is often the key to them achieving a good grade at school. Often, the lack of understanding from the previous years can block a student in his comprehension of a lesson and make him or her feel demotivated by the subject. I believe I can manage to fix this by providing them with the right support and most importantly, going back to gaps in their knowledge that occurred in previous years. It is necessary for them to have a good foundation in their subject to help them move forward and be able to grasp more complex ideas.

During my lessons, I will work at the pace of the students as it can help them not to be under pressure. I can give them all the support they need with an ear to listen and lots of original exercises.
Experience / Qualifications
Nine years of private tutoring. At the moment, I'm doing my GCSE in order to be able to start a PGCE next year and become an official teacher in the UK.
Age
Teenagers (13-17 years old)
Adults (18-64 years old)
Student level
Beginner
Intermediate
Duration
60 minutes
120 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
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Python Fundamentals (variables, control structures, functions)
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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- 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.
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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.
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Learn Python with a structured, hands-on approach! Whether you are a absolute beginner looking to start coding, a student needing help with coursework, or a professional aiming to automate tasks, these lessons are tailored for you.What we will cover:
Core Fundamentals: Variables, loops, functions, and data structures.Object-Oriented Programming: Building reusable and clean code.Real-World Projects: Creating scripts, data analysis, or web scraping based on your goals.

Problem Solving: Learning how to debug and think like a programmer. Lessons are highly interactive. We will write code together from day one, and you will receive practical exercises after every session to build your confidence.
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