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Since September 2025
Instructor since September 2025
Learn Programming and Web development from zero to hero!
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From 25 C$ /h
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Code feeling like a puzzle? 🧩 Learn smart, build real, and power up your skills + portfolio.

Hi, I’m Ahsan, a Software Engineer and Full-Stack Web Developer with 8+ years of hands-on experience in both front-end and back-end technologies like HTML, CSS, Bootstrap, JavaScript, jQuery, Vue, PHP, Laravel, CodeIgniter, and MySQL.

Whether you're stuck on a project, struggling with assignments, or simply tired of endless tutorials that leave you more confused, you're just one click away from getting real help, real fast.

I help students and self-learners not just “learn to code,” but truly understand it. You’ll gain clarity on concepts like programming fundamentals, OOP, Internet Programming, and full-stack development, all explained in simple, practical terms.
Extra information
We’ll also explore real-world skills: building dynamic websites, connecting APIs, validating forms, fixing bugs, deploying projects, and even version controlling with Git & GitHub. Whether you're prepping for exams, working on a capstone, or trying to crack your first freelance job, I’ll guide you every step of the way.

This is where your confusion ends, and your confidence begins.
Clear concepts. Clean code. Real results. Let’s start today. 💡
Location
location type icon
Online from Pakistan
About Me
Hi, I’m Ahsan, a Software Engineer and Full-Stack Web Developer with 8+ years of hands-on experience in both front-end and back-end technologies like HTML, CSS, Bootstrap, JavaScript, jQuery, Vue, PHP, Laravel, CodeIgniter, and MySQL.

Whether you're stuck on a project, struggling with assignments, or simply tired of endless tutorials that leave you more confused, you're just one click away from getting real help, real fast.
Education
Bachelors in Software Engineering.

I help students and self-learners not just “learn to code,” but truly understand it. You’ll gain clarity on concepts like programming fundamentals, OOP, Internet Programming, and full-stack development, all explained in simple, practical terms.
Experience / Qualifications
Computer Science
Instructor
2022 – Present


Computer Science
Instructor
2020 - 2022

Software Engineer
FEB 2025 - Present
Age
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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Python is one of the best, if not the best, language to start learning programming. It is also one of the most widely used languages today, especially in cutting-edge areas such as machine learning.

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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

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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.

Pancyprian Exams – Computer Science

Structured exam preparation focusing on:
• understanding the syllabus
• correct algorithmic thinking
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• 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
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📖 Detailed program content

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🚀 Learner's result

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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

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3- DATA PREPARATION AND EXPLORATION
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• 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
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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
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• 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

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

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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.

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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.

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Python is one of the best, if not the best, language to start learning programming. It is also one of the most widely used languages today, especially in cutting-edge areas such as machine learning.

This popularity means that Python is constantly evolving. It offers a wide range of tools and libraries, which are free and very varied.

As an aeronautical engineer, I like to share my knowledge and derive satisfaction from it by teaching and motivating others.

I'm used to working with people of different ages. I believe in the importance of segmenting learning, visualizing progress, setting concrete goals and practicing regularly.

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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
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Don't settle for anything less than excellence.
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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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Every lesson is meticulously planned in advance to ensure that it aligns with your goals and targets areas for improvement.
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verified badge
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Let's turn your ideas into working Python projects!
verified badge
Master Python with Personalized Courses

Discover the art of programming with Python courses tailor-made to meet your specific needs. Whether you are a beginner, intermediate or professional, my lessons are suitable for all levels.

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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Discover programming lessons suitable for children! With a fun and educational approach, my lessons allow young minds to dive into the fascinating world of programming. Provide your children with an enriching learning opportunity in a fun and stimulating environment.
verified badge
This course is designed to introduce students aged 7 to 16 to the world of programming through two of the most widely used and industry-relevant languages: C++ and Python.

The class provides a structured, age-appropriate pathway into programming, whether the student is a complete beginner or already exploring coding through platforms like Scratch or Code.org. Emphasis is placed on understanding logic, building problem-solving skills, and writing real code in a supportive, project-based environment.

Taught by an engineering student with hands-on experience in both C++ and Python, this course empowers students to explore the power of code and build a strong foundation in computational thinking — essential for future studies in engineering, robotics, AI, or game development.
verified badge
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Unlock the power of one of the most influential programming languages in computing history! Whether you're an absolute beginner or looking to deepen your expertise, this comprehensive C/C++ course delivers structured learning from fundamentals to advanced concepts that power operating systems, game engines, and high-performance applications.

## **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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## **Your Learning Journey**

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."

## **Transform Your Career Today**

C/C++ skills remain in high demand across industries from finance to gaming to IoT. By mastering these foundational languages, you'll develop problem-solving abilities that translate to any programming environment.
verified badge
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:
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• 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
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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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This module is a crucial step for any web developer wishing to move from simple DOM manipulation to mastering modern frameworks. The objective is clear: to understand the "invisible foundations" of the language in order to write shorter, more readable code and, above all, be ready to code professionally in React.

🎯 Training Objectives

1- Demystify the modern syntax (ES6+) often used in React.
2- Increase efficiency by using the most powerful syntactic shortcuts.
3- Secure your code to avoid frequent bugs related to missing data.
4- Mastering asynchronicity to manage data calls (API).

📖 Detailed program content

The course is divided into 13 key concepts, illustrated by comparative examples (classic syntax vs. modern syntax) and concrete use cases in React:

1- Ease of writing: Use of Template Literals (`backticks`) for dynamic character strings and Shorthand property names to simplify the creation of objects.

2- Logic and Functions: Mastery of Arrow => Functions (arrow functions) and their implicit return, essential for React components and hooks.

Data manipulation:

1- Destructuring (decomposition) to properly extract data from objects and arrays (e.g., Props and States).

2- Rest & Spread Operators (...) to copy arrays or merge objects without modifying the original (concept of immutability).

Code robustness:

1- Managing default parameter values.

2- Advanced security with Optional Chaining (?.) and Nullish Coalescing (??) to prevent application crashes.

3- Functional Programming: Intensive use of array methods (.map(), .filter(), .reduce(), .find()) to transform data into user interfaces.

4- Architecture and Asynchronism: Code organization via modules (Import/Export) and API request management with Promises and Async/Await.

🛠️ Teaching method: "Learning by doing"

This course is not just about theory. It includes:

The "Interstellar Dashboard" Exercise: A 15-minute thematic case study where students manipulate data from space missions. This allows them to immediately apply destructuring, filtering, and asynchronicity to a real-world project.

The Interactive Quiz: A series of 10 questions designed to validate understanding of each concept before moving on. Each question presents real-world scenarios that developers will encounter in React.

🚀 Learner's result

By the end of this course, students will not only "know" JavaScript; they will understand why and how each syntax is used to build efficient React components. They will leave with a solid foundation to confidently tackle Hooks (useState, useEffect) and complex state management.

Format: Clean visual presentation, coloured syntax for code, and focus on readability.
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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.
Good-fit Instructor Guarantee
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