Learn Three.js / 3D web development with TypeScript

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From 69 € /h

Do you want to develop interactive 3D applications directly in the browser or integrate Three.js into an existing web project?

In this course, you'll learn practical skills for building modern 3D applications with Three.js and TypeScript. Depending on your goals, we can start with the basics or work directly on an existing project.

The focus is not just on getting a scene to work somehow, but on understanding how 3D web applications are structured and how Three.js can be meaningfully integrated into modern web applications.

Possible topics include:

• Fundamentals of Three.js
• Scenes, cameras and renderers
• Geometries and materials
• Light and shadow
• Textures and assets
• Loading and using 3D models
• GLTF and GLB models
• Animations
• User interactions with 3D objects
• Raycasting and object selection
• Camera control and navigation
• Performance optimization
• Structure of larger Three.js applications
• TypeScript in 3D projects
• Integration of Three.js into Angular applications
• Debugging and troubleshooting

If you already have your own project, we can work with it directly. This could be, for example, a product configurator, an interactive visualization, a 3D model in the browser, or another web application with 3D content.

I have used Three.js myself in professional projects, including the development of a web-based 3D configurator. This allows me to impart not only the fundamentals but also practical experience that is important in real-world applications.

The course is suitable for developers, students and technically interested beginners who already have basic JavaScript or TypeScript knowledge and want to get started in 3D web development or deepen their existing knowledge.

Location

Online from Austria

About Me

I am a freelance software developer and have been working professionally in modern web development for many years. My focus is on Angular, TypeScript, and the development of large web applications.

I've been working with Angular since 2016. Today, I run my own software agency, WeekndDevs, developing both custom software solutions for clients and our own software products. This means I'm familiar not only with the theory behind individual technologies but also with the challenges that arise in real-world projects – from architecture and code quality to debugging and performance, as well as testing, deployment, and long-term maintainability.

When teaching, it's important to me to explain technical topics in a clear and practical way. I don't want to simply provide a ready-made solution, but rather show why something works and how to arrive at a good solution independently.

I tailor the lessons to each student's individual level of knowledge. Beginners can start with the basics, while with advanced students or developers we can work directly on specific problems, existing code, or architectural questions.

It is particularly important to me that questions can be asked at any time and that any uncertainties are truly clarified. The goal is not to cover as much material as possible in a short time, but rather to understand the connections and then be able to apply what has been learned independently.

My areas of expertise include:

• Angular and TypeScript
• JavaScript, HTML and CSS
• modern front-end architecture
• APIs and full-stack web development
• NestJS and PostgreSQL
• Git, testing and debugging
• Performance and code quality
• professional development tools and workflows

The course is suitable for school pupils, HTL/FH/university students, career changers as well as developers already working in the field.

If you are working on your own project or are stuck on a specific task, we can gladly work directly with your existing code and develop a solution together.

Education

Alpe-Adria University in Klagenfurt, Applied Computer Science from 2012 to 2016.
IT-HAK diploma with a focus on information technology and business.

Since then, continuous professional development in modern web development, especially in Angular, TypeScript, JavaScript, software architecture, testing and full-stack development.

Experience / Qualifications

Since 2017 I have been self-employed in software development and have been working intensively with Angular since 2016.

Over the course of my professional career, I have worked on various web applications and larger software projects, gaining particular experience in Angular, TypeScript, frontend architecture, APIs, databases, testing and performance optimization.

Besides development, my responsibilities also included training and supporting other developers. I guided developers on their introduction to Angular and developed training materials for Angular.

Today I run my own software agency, WeekndDevs, and develop individual software solutions as well as my own products such as Turniermeister, Bierblock and else.events.

This allows me to not only impart theoretical knowledge in class, but also to explain many topics based on real experiences from professional software projects.

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

60 minutes

The class is taught in

German

English

Skills

Computer programming

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

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Full-stack web development with Angular, NestJS & PostgreSQL

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• clean project and software architecture
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electrical engineering (transformers, electrical machines, switching power supply)
C / c ++ programming, Assembler, ARM, STM32
renewable energy (wind, PV)
engineering Sciences
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Resumption and deepening of fundamental concepts through exercises with course reminders.

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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)

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Control or manage your own hardware?

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Master Python with Personalized Courses

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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:

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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**

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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.

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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

-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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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