Explore how modern JavaScript uses classes to organize logic, model real-world objects, and build reusable, maintainable code. Work with constructors, methods, inheritance, and encapsulation to understand how object-oriented patterns shape application structure. Apply advanced class features and build practical class designs that strengthen your ability to write clean, scalable JavaScript.

Learn to handle dates and times in JavaScript using Day JS. Practice parsing user input, formatting dates for display, modifying time values, and comparing dates.

Learn how to use proven design patterns to write cleaner, more maintainable JavaScript. Explore creational, structural, and behavioral patterns through small, practical examples that show how each pattern solves real coding challenges.

Master the power of lambda expressions in Java to write concise, expressive, and modern code. This course guides you from the basics of functional interfaces to advanced lambda usage, including real-world applications and best practices.

Learn how C programs interact directly with the Linux kernel using system calls. This course covers the fundamentals, practical usage, and advanced topics, empowering you to write robust, efficient, and secure Linux applications.

Master practical MLOps workflows for deploying, tracking, and maintaining machine learning models in production. Learn to use MLflow, FastAPI, Docker, Airflow, and scikit-learn to automate and scale your ML projects.

Build modern, responsive, and visually polished React applications using Material UI. Learn to structure interfaces with core components, design flexible layouts, manage forms, and apply responsive behavior. Customize themes, refine visual styles, and develop practical features through focused, real-world mini projects. Gain the confidence to create clean, accessible, production-ready UIs with MUI.

A hands-on course teaching practical machine learning techniques for time series forecasting using tree-based and boosting models. Learn windowing, feature engineering, temporal validation, and multi-step forecasting strategies with Python and scikit-learn.

Uncover a complete, practical framework for turning site traffic into measurable growth. You'll start by understanding the psychology behind user decisions, key metrics that signal performance, and the patterns that guide people through a digital journey. Explore advanced optimization techniques including A/B testing, heatmaps, personalization, mobile performance, and iterative improvements. The final modules expand CRO beyond a single page, showing how to optimize full funnels, traffic sources, retargeting flows, and multi-device experiences while building a mindset of continuous experimentation.

A comprehensive course dedicated to understanding, designing, and applying methods in Java. Explore method fundamentals, parameter passing, overloading, recursion, and best practices through a blend of theory and hands-on coding challenges.

Dive deep into the world of SQL query optimization by mastering the EXPLAIN statement and understanding how query planners work. This course blends engaging theory with hands-on, real-world tasks to help you analyze, interpret, and optimize SQL queries using EXPLAIN and related tools.

Master the scikit-learn library by learning its API, core abstractions, and engineering patterns. Focus on syntax, structure, and workflow to confidently build, compose, and inspect machine learning pipelines using scikit-learn.

Gain a rigorous mathematical understanding of neural networks as function approximators. Explore their linear-algebraic structure, approximation power, and the fundamental role of depth in expressivity—without implementation or training details.

A comprehensive theoretical exploration of foundational and advanced network protocols, including TCP, UDP, DNS, TLS, and HTTP/3. This course delves into protocol mechanics, design principles, and deep technical insights, supported by pseudo code and conceptual quizzes.

A rigorous, mathematics-driven exploration of the theoretical foundations, methods, and limitations of neural network compression. This course focuses on intuition, formal reasoning, and the interplay between information theory and deep learning model design.