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Since November 2025
Instructor since November 2025
Website Designing using html JavaScript and css, game building using scratch
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From 22 C$ /h
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🌐 Learn Web Development & Game Creation — Fun and Practical Tutoring!

Are you or your child interested in learning how to build websites or create games from scratch?
Join my personalized Web Development and Game Building Tutoring Program — designed for both beginners and kids who want to learn through creativity and hands-on projects.

💻 What You’ll Learn:

For Web Development (Teens & Beginners):

Build real websites using HTML, CSS, and JavaScript

Learn modern web design and responsive layouts

Create interactive pages — buttons, forms, animations, and more

Understand how websites actually work behind the scenes


For Kids (Ages 7–14) — Game Building with Scratch:

Design your own games and stories using Scratch

Learn coding logic with fun drag-and-drop blocks

Improve creativity, problem-solving, and storytelling skills


🎯 Why Learn With Me:

Step-by-step lessons — easy to follow, project-based learning

Flexible schedule (online or in-person)

No prior experience required — just curiosity!

By the end, you’ll have real projects you can proudly show off


📅 Start Learning Today!

Whether you want to become a web developer or spark your child’s interest in coding, this program makes learning fun, practical, and future-ready.
Extra information
Laptop
Location
location type icon
Online from Ethiopia
About Me
👋 About Me

Hi,

I’m Befekadu — a web development tutor and frontend developer passionate about helping students turn ideas into real, working projects. I specialize in HTML, CSS, and JavaScript, teaching students how to build modern, responsive, and interactive websites from scratch.

I also love introducing younger learners to the world of coding through Scratch game development, where kids learn programming logic, problem-solving, and creativity in a fun and engaging way.

With a background in software engineering and hands-on experience building real-world applications, I focus on practical, project-based learning that builds confidence and skills step by step.

Whether you’re a beginner wanting to start your journey in web development or a parent looking to inspire your child through coding, I make learning both simple and enjoyable.
Education
I hold a Bachelor of Science degree in Computer Science from Hawassa University, where I built a strong foundation in programming, algorithms, database management, and system design. During my undergraduate studies, I developed a deep interest in software development and problem-solving, which led me to work on several academic and personal projects involving web technologies and application design.

To further enhance my technical expertise, I pursued a Master of Science degree in Software Engineering at Addis Ababa University. My graduate studies focused on advanced software architecture, development methodologies, and system optimization. This program strengthened my ability to design scalable, efficient, and user-centered applications, bridging theory with real-world implementation.

Through both degrees, I gained valuable experience in analytical thinking, teamwork, and modern software practices. This academic journey has shaped my approach to teaching and development — combining technical depth, clarity, and creativity to help learners build practical, future-ready skills.
Experience / Qualifications
I have over eight years of teaching experience, working with students across different age groups and academic levels. Throughout my career, I have taught subjects related to computer science, programming, and digital literacy, helping learners build both foundational knowledge and practical skills. My teaching approach focuses on hands-on learning, encouraging students to understand core concepts through real projects and problem-solving activities.

Over the years, I have guided students in areas such as web development, basic coding, and game creation, fostering curiosity and confidence in technology. My experience has also strengthened my ability to adapt lessons to individual learning styles, ensuring that every student progresses effectively and develops a lasting interest in technology and innovation.
Age
Children (7-12 years old)
Teenagers (13-17 years old)
Student level
Beginner
Intermediate
Advanced
Duration
60 minutes
The class is taught in
English
Amharic
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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English text below

PhD Candidate in Computer Science – Private Tutoring & Pancyprian Exams

I am a PhD candidate in Computer Science offering private tutoring for high school students (Pancyprian Exams – Computer Science) and university students.

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• 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
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• Exploratory data analysis using descriptive statistics, Matplotlib, Seaborn, and graphical interpretation

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• Linear algebra, vectors, matrices, derivatives, optimization, probability, and statistics
• Loss functions, gradients, distance measures, regularization, likelihood, and model complexity
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6- UNSUPERVISED LEARNING
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• Underfitting, overfitting, bias–variance trade-off, class imbalance, feature engineering, feature selection, scaling, and regularization

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-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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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.

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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.

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4- Use a builder (like Elementor) to create modern and dynamic pages

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6- Import a pre-built demo to save time

7- Put your site online and learn good maintenance practices

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2- Getting started with the WordPress interface

3- Management of pages, articles, media, extensions and settings

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• Lessons are available in person or online.
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PhD Candidate in Informatics – Private Lessons & Pancyprian Exams

I am a PhD Candidate in Informatics and I offer private lessons in Informatics to High School students (Pancyprian Exams) as well as to University students, with an emphasis on correct understanding and methodical thinking.

Pancyprian Exams – Informatics

Systematic preparation with an emphasis on:
• understanding of the material
• correct algorithmic thinking
• methodology for solving problems
• analysis of old Pancyprian exam questions

We cover, for example: pseudocode, tables, repetitions, control structures and common exam errors.

Students & General Computing

Support in:
• Programming (C / C++ / Python)
• Operating Systems
• Computer Architecture
• Code Understanding & Debugging

In-person or online courses, with emphasis on understanding and proper study organization.

English text below

PhD Candidate in Computer Science – Private Tutoring & Pancyprian Exams

I am a PhD candidate in Computer Science offering private tutoring for high school students (Pancyprian Exams – Computer Science) and university students.

Pancyprian Exams – Computer Science

Structured exam preparation focusing on:
• understanding the syllabus
• correct algorithmic thinking
• exam-oriented problem-solving
• analysis of past Pancyprian exams

Topics include pseudocode, arrays, loops, control structures, and common exam mistakes.

University & General Computer Science

Support in:
Programming (C/C++/Python)
• Operating Systems
• Computer Architecture
• Code understanding and debugging

Lessons are available in person or online, with emphasis on understanding concepts rather than memorization.
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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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Learn how to build modern websites and mobile applications from scratch and develop the practical skills needed for real-world projects.

In this Frontend & Mobile Development course, I’ll guide you step by step through the technologies and tools commonly used by developers, including HTML, CSS, JavaScript, Angular, Git, GitHub, VS Code, and mobile application development.

You’ll learn how to create responsive and interactive websites, build structured applications with Angular, manage your code using Git and GitHub, and use VS Code efficiently for development. We’ll also explore how frontend technologies can be used to create mobile applications.

The course is suitable for beginners and intermediate learners. Lessons are practical and project-based, so you’ll learn by actually building websites and applications rather than only studying theory.

My goal is to help you understand how and why the code works, improve your problem-solving skills, learn professional development practices, and gain the confidence to create your own projects independently.

Lessons can be adapted to your experience level, learning pace, and personal or career goals.
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