Discover how to write CUDA C++ applications that efficiently and correctly utilize all available GPUs in a single node, dramatically improving the performance of applications and making the most cost-effective use of systems with multiple GPUs.

Explore how to employ advanced data engineering tools and techniques with GPUs to significantly improve data engineering pipelines.

A course that builds a foundational understanding of the concept of website accessibility and allows students to explore and implement the standards needed to create accessible websites.

In this course, learners will prepare data for, implement, and optimize three advanced clustering models in Python while comparing their different use cases. In particular, this course focuses on the suitability of different clustering methods for different kinds of data: numerical, categorical, and mixed. Learners will distinguish between K-modes, mean-shift, and K-prototypes models, developing their understanding of when each model will best meet their needs.

This course build on the subject of Convolutional Neural Networks and dives into the complex pre-trained state- of-the-art CNN architectures. It also gives students a preview of what transfer learning is and why it is such a powerful concept in Deep Learning. By the end of this course students will be able to have implemented and explored pre-trained models such as ResNet, VGG16, and Inception3.

This course continues on tackling topics in deep learning for text analysis. In this course students will be getting to know how to use and implement Gated Recurrent Units (GRUs) and model and predict longer sequences of text by leveraging Seq2Seq models.

A course that builds an advanced understanding of Git and version control systems. By the end of the course students will be able to use key Git commands and interact with remote repositories like GitHub, Bitbucket, and GitLab for efficient tracking and maintaining of software code.

An introduction to GraphQL advanced concepts and tools using Node.js. Learn how to use Apollo graph platform to manage GraphQL.

An introduction to GraphQL advanced concepts and tools using Python. Learn how to use Apollo graph platform to manage GraphQL.

This course introduces experienced Python programmers to some of the language’s more advanced features. If you have been using Python for more than six months, and want to understand and explore the more advanced features of the language — making your programs more expressive and powerful — then this course will help you.

Provides a deep-dive into internal features of the Python programming language as related to asynchronous computation, concurrency, efficiency, functional programming, and object-oriented design.

A course that builds on the foundations of React framework and expands learners` skills to more advanced concepts like Hooks, Context, Refs and RenderProps.

A course that builds on the foundations of React framework and expands learners` skills to more advanced concepts like code splitting, high order components, portals and error boundaries.

This course is designed for students looking to deepen their understanding of creating visualizations and interpreting data in Tableau. By the end of the course, students will be able to incorporate Tableau’s auto-generated fields in their visualizations, construct calculations in a variety of ways, and gauge users’ needs when developing Tableau-based products.

Learn to detect anomalies in large data sets to identify network intrusions using supervised and unsupervised machine learning techniques, such as accelerated XGBoost, autoencoders, and generative adversarial networks (GANs).

Discover how to identify anomalies and failures in time-series data, estimate the remaining useful life of the corresponding parts, and use this information to map anomalies to failure conditions.

Learn how to apply seasonal analysis and ARIMA models and how to decompose and identify seasonal and non- seasonal factors all while learning the nuances of building sophisticated time series models.

An introduction to Authentication concepts using Node.js.

An introduction to Authentication concepts and how it can be implemented using Python.

This course takes students through a journey into the world od autoencoders - a set of powerful deep learning models that have a special place in the world of image analysis. By the end of this course students will be able to navigate through the application space of autoencoders and implement autoencoders to perform tasks such as image denoising and more.