Python is used in a wide variety of settings. One popular use is as a language for carrying out a variety of system-administration tasks — from carrying out bulk file operations to monitoring systems to provisioning new servers. In particular, many Unix administrators have started to use Python instead of the traditional Unix shell.

Python has become extremely popular in the last decade; on Stack Overflow, it is both the most popular programming language and the fastest-growing one. However, using Python well requires practice and experience. Its syntax might be clear and straightforward, but it takes time to learn how and when to use different techniques, which idioms are appropriate in different contexts, and how to combine techniques to solve real-world problems.

Introduces participants to the foundations of Deep Learning through PyTorch. Participants practice constructing neural networks of various levels of complexity to connect the core ideas to their realization in practical applications (e.g., image processing, natural language processing, etc.).

Introduces the open-source Python RAPIDS libraries for accelerating computation with GPUs (graphics processing units). Participants practice using the RAPIDS libraries for common ETL and machine learning workloads without having to program with low-level languages (e.g., C/C++).

A course that builds on the React Ecosystem. It walks through how to integrate APIs using various libraries into a React application.

A course that builds on the foundations of React framework and expands learners` skills to more advanced concepts.

A course that builds on the React Ecosystem. It walks through React Router, a fully-featured client and server- side routing library for React.

A course that builds on the React Ecosystem. It explains how state management works in React and goes over the Redux state management library.

A course that expands on your react knowledge to make your own styled components and leverage material UI library.

A course that introduces to testing simple and complex React applications with React Testing Library.

This course provides an overview of how recommender systems work and teaches students how to build effective models. By the end of this course, students will be able to explain the key assumptions underlying recommender systems and build and evaluate them based on real data.

Regular expressions (“regexps”) make it possible to find patterns inside of text. Whether you’re trying to find URLs in a document, IP addresses in a log file, or telephone numbers in an address book, regular expressions can be invaluable — and thus are included in most modern programming languages, such as Python, Ruby, and JavaScript, as well as .NET, Java, and C++.

This course covers the specialized branch of machine learning and deep learning called reinforcement learning (RL). By the end of this course students will be able to define RL use cases and real world scenarios where RL models are used, they will be able to create a simple RL model and evaluate its performance.

An introduction to RESTful conventions for building and querying an API, using Node.js.

An introduction to fundamental concepts on building RESTful APIs using Python.

The RShiny package offers data scientists and analysts tremendous control over the end user's experience of interactive visualizations. In this course, learners will use RShiny as a comprehensive tool to build, structure, and publish an entire data dashboard. After being introduced to RShiny's structure, learners will generate interactive visualizations. Learners will also be introduced to core principles of front-end, user-facing interface design in order to optimize their dashboard's usability.

Learn the tools and techniques needed to write CUDA C++ applications that can scale efficiently to clusters of NVIDIA GPUs.

A course that goes over the basic concepts and terminologies of elasticsearch and how to write queries for elasticsearch.

This course covers the intermediate concepts of natural language processing like sentiment analysis. By the end of this course students will be able to build classification model for textual data.

A course that builds a foundational understanding of basic concepts of Search Engine Optimization (SEO) and best practices of effective SEO.