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Since February 2026
Instructor since February 2026
Practical Programming & Web Development (Python, JavaScript, CSS, WordPress & UI/UX)
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From 26 € /h
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This online programming class is designed for students who want to build real technical skills in computer science and web development — not just learn theory, but actually create and understand how systems work.

I teach:

• Python programming (fundamentals, logic building, problem-solving, data handling)
• JavaScript (core concepts, DOM manipulation, interactivity)
• CSS (styling, layout systems, responsive design)
• Website Development (front-end foundations and structure)
• WordPress Development (customization, site building, content management)
• UI/UX Fundamentals (design thinking, layout clarity, user experience principles)

Students learn how to think computationally, break down problems logically, and build structured solutions. Lessons are practical and project-based, meaning students don’t just watch — they build.

For beginners, we focus on strong foundations: understanding how code works, writing clean syntax, and developing confidence in debugging.
For intermediate learners, we move into structured projects such as creating simple websites, improving layout design, adding interactivity, and understanding how front-end components connect.

My approach emphasizes clarity, structure, and application. I help students understand not only what to type, but why it works — which is the difference between copying code and truly understanding it.

This class is suitable for:
• School students exploring computer science
• Beginners transitioning into tech
• Learners preparing for academic computer science courses
• Individuals interested in web development skills

All sessions are fully online, interactive, and tailored to the student’s pace and goals.
Extra information
Classes are conducted fully online.
Students receive structured lesson plans, practice materials, and post-class feedback.
Stable internet connection and a quiet learning space are recommended.
Location
location type icon
Online from Singapore
About Me
Apprentus is built on relationships, and I strongly believe meaningful learning begins with trust and understanding. I am an experienced online educator with over 10 years of teaching experience, having worked with more than a thousand students across different countries and academic systems. Teaching for me is not just about delivering content — it is about understanding how each student thinks, learns, and builds confidence.

My classes are structured, engaging, and tailored to individual needs. Some students need step-by-step guidance to close learning gaps, others need challenge and depth to reach higher academic goals. I adapt my approach depending on the learner, ensuring that lessons are clear, interactive, and productive.

I particularly enjoy working with students who are serious about improving — whether they are preparing for exams, strengthening foundations, or aiming for academic excellence. Parents appreciate that I provide clear explanations, consistent feedback, and a supportive environment where students feel comfortable asking questions.

When students work with me, they gain clarity, structure, and confidence — not just short-term improvement, but long-term academic growth.
Education
Bachelor’s Degree in Electronic & Electrical Engineering

Strong academic background in Mathematics, Physics, analytical reasoning, and problem-solving

Continuous professional development in online education, international curricula, and digital learning tools

Experience integrating structured lesson planning, assessment strategies, and curriculum alignment across multiple academic systems
Experience / Qualifications
Over 10 years of online teaching experience across English, Mathematics, Science, and Programming

Taught more than 1,000 students globally across Asia, Europe, Africa, the Middle East, and the Americas

Extensive experience supporting students in the following curricula:

• Cambridge Primary & Lower Secondary
• IGCSE
• GCSE
• IB (MYP & DP)
• A-Level
• Common Core
• International and national school programs

Experience in external exam preparation, academic skill building, and structured lesson customization

Specialized in identifying learning gaps, strengthening foundational understanding, improving exam performance, and building student confidence

All sessions conducted online with interactive tools, structured materials, and consistent performance tracking
Age
Infants (0-3 years old)
Preschool children (4-6 years old)
Children (7-12 years old)
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Intermediate
Advanced
Duration
30 minutes
45 minutes
60 minutes
90 minutes
120 minutes
The class is taught in
English
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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I support learners studying under curricula such as Cambridge, IGCSE, GCSE, IB (MYP & DP), A-Level, and other international programs. Whether a student needs help catching up, preparing for exams, or aiming for higher achievement, lessons are tailored to their pace and goals.

With structured practice, guided problem-solving, and exam-focused strategies, students gain both clarity and confidence.
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I have worked with students following Cambridge, IGCSE, GCSE, IB, and other national programs. Lessons are interactive, structured, and designed to close learning gaps while strengthening foundational understanding.

By combining clear explanations, guided practice, and exam preparation strategies, I help students move from confusion to clarity and from uncertainty to confidence.
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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
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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

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• 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

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Python class from basics to the advance from the students perspective with proper deep understanding of concepts.
Topics to be covered:
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## **Why Choose This C/C++ Program?**

**Industry-Relevant Curriculum:** Learn expert guidance on the design of effective classes, functions, templates, and inheritance patterns that form the backbone of professional C++ development. Move beyond basic syntax to understand how to write clean, efficient, and maintainable code that stands the test of time.

**Templates & Generic Programming Mastery:** Go beyond introductory material with in-depth coverage of templates—the cornerstone of modern C++—enabling you to create robust, reusable code components that work across multiple data types. Discover how function templates, class templates, and variadic templates work to maximize your coding efficiency.

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Our structured path takes you from writing your first "Hello World" program through advanced template metaprogramming, with special attention to modern C++ standards (up to C++20). You'll gain the confidence to tackle complex programming challenges and understand the "why" behind effective C++ practices—not just the "how."

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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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I'm a working MEP engineer, currently building Python automation for Revit workflows daily - plan checks, model coordination, and repetitive drafting tasks. I teach other engineers, architects, and BIM professionals how to do the same, using pyRevit and real project workflows, not toy examples.

Topics include:
pyRevit fundamentals and setup
Automating repetitive Revit tasks (model checks, plan generation, data extraction)
Writing custom scripts for your firm's specific workflows
Applying Python automation to real MEP/BIM projects
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📊Microsoft Office: To establish your office skills, manage data via Excel, write professional reports with Word, and prepare impressive presentations with PowerPoint.

📐 AutoCAD: To learn engineering drawing and design two-dimensional and three-dimensional plans with high accuracy and speed.

🛣️ Civil 3D: To learn road design, network design, surveying, and quantity surveying in a scientific and practical way
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Microsoft Excel is a powerful Microsoft corporation application that helps one do various mathematical, statistical and logic operations on data sets. It's a powerful tool for data analysis.

In this lesson you will be introduced to Microsoft Excel, it's importance and the basic features of the Microsoft Excel user interface.
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Have you ever wanted to learn how websites are created but didn’t know where to start? This class is designed for complete beginners who want to learn web development in a simple, practical and enjoyable way.

I will guide you step by step through the fundamentals of HTML, CSS and JavaScript, helping you understand how websites are structured, styled and made interactive.

In this class, you will learn how to:

- Understand the basics of web development and how websites work
- Use HTML to create the structure of a webpage.
- Use CSS to add colours, layouts, fonts and styles.
- Get started with JavaScript and understand basic website interactivity.
- Write and organise simple code.
- Build your first basic webpage through practical exercises.

No previous coding experience is required. We will learn at your pace, with clear explanations, hands on practice and time to ask questions.

My goal is to help you build a strong foundation in web development and gain the confidence to continue learning and creating your own websites.
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Are you interested in learning Python but don't know where to start? This course is designed to help you understand the fundamentals of Python programming in a simple, practical, and enjoyable way.

Python is one of the most popular programming languages in the world. It is widely used in software development, automation, data analysis, artificial intelligence, and web development.

Whether you are a complete beginner, a student, or someone looking to develop new technical skills, these lessons will help you build a strong foundation in programming.

You will learn
1. Introduction to Python
2. Python Fundamentals
3. Conditional Statements and Loops
4. Working with Data
5. Functions and Practical Programming
6. Mini Projects
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Python class from basics to the advance from the students perspective with proper deep understanding of concepts.
Topics to be covered:
Basic Python and Programming fundamentals
Memory Management
OOPs
Data Structure and Algorithms
Libraries
Data analysis (using libraries Pandas, Numpy, etc.)
AI (ML, DL, RL, LLMs, RAG, Langchain etc.)

Maths using visualization of geometry, algebra, calculus, etc.
Good-fit Instructor Guarantee
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