Decision tree models are classification algorithms that sort novel data into categories based on iterative splitting, like the branches of a tree, according to input parameters. In this course, learners will identify use cases for decision trees in Python. They will wrangle data and implement a decision tree model before attempting to evaluate its effectiveness. Finally, learners will use their knowledge of the mathematics behind decision trees to tune the model and improve its classificatory function.

Decision tree models are classification algorithms that sort novel data into categories based on iterative splitting, like the branches of a tree, according to input parameters. In this course, learners will identify use cases for decision trees in R. They will wrangle data and implement a decision tree model before attempting to evaluate its effectiveness. Finally, learners will use their knowledge of the mathematics behind decision trees to tune the model and improve its classificatory function.

This course continues on tackling topics in deep learning that address specific problem types. In this course students will be getting to know RNNs and LSTMs - types of neural networks that are often used for solving problems in text analysis.

A course that goes over the principles and architectural patterns of software design and development.

A course that covers theory and implementation on a specific cloud platform covering topics on distributed data storage systems. Learners will be able to dive into the nature of storing and processing data at scale using tools like Hadoop on a selected cloud platform. This course will allow students to get a great foundation for creating and managing distributed data storage resources.

A course that builds a foundational understanding of the domain name system, how to host a webpage and add a custom domain name.

This course covers an overview of ensemble learning methods like random forest and boosting. At the end of this course, students will be able to implement and compare random forest algorithm and boosting.

This course covers an overview of ensemble learning methods like random forest and boosting. At the end of this course, students will be able to implement and compare random forest algorithm and boosting.

A course that explores Enzyme, which is a JavaScript utility for React applications. The course equips users to simulate runs and test React components` outputs.

This course helps students to identify the most impactful features for your model. It will build upon foundational machine learning techniques to hone predictive skills and discover critical danger points in patterns. By the end of this course, students will be able to determine key features in models.

A theoretical course covering topics on how to handle data at scale and the different tools needed for distributed data storage, analysis, and management. Learners will be able to dive into the vast world of data and computing at scale and get a comprehensive overview of distributed computing.

Explore how to use Numba the just-in-time, type-specializing Python function compiler to accelerate Python programs to run on massively parallel NVIDIA GPUs.

Learn how to accelerate and optimize existing C/C++ CPU-only applications to leverage the\npower of GPUs using the most essential CUDA techniques and the Nsight Systems profiler. You’ll learn how to write code, configure code parallelization with\nCUDA, optimize memory migration between the CPU and GPU accelerator, and implement the workflow that\nyou’ve learned on a new task—accelerating a fully functional, but CPU-only, particle simulator for observable\nmassive performance gains

Find out how to write and configure code parallelization with OpenACC, optimize memory movements between the CPU and GPU accelerator, and apply the techniques to accelerate a CPU-only Laplace Heat Equation to achieve performance gains.

Learn how to perform multiple analysis tasks on large data sets using RAPIDS, a collection of data science libraries that allows end-to-end GPU acceleration for data science workflows.

This 12-hour workshop educates participants on the fundamentals of data science and how to apply them in such a way that it is relevant even to those who will neither manage nor consume data regularly. Attendees will learn: data science concepts and associated terminology; why data science is important; what it means to work in a data-driven culture including the skills necessary to thinking critically about data; common issues in data collection and analysis such as bias, data gaps, and imprecision; strategies for interpreting data visualizations produced by others; and foundational steps those who are not data scientists can take to incorporate data analysis into their work.

This course is an introduction to the what, why, and how of data science intended for learners with little to no prior familiarity. Learners will discuss the role that data and analytics play in modern organizations and track data from collection through preparation and visualization all the way through modeling and reporting. By the end of this course, learners will have begun to understand the components of a robust data culture, how data is used in accomplishing goals, and how to identify allies with expertise to assist with data-driven tasks.

Learn how deep learning (DL) works through hands-on exercises in computer vision and natural language processing (NLP). You will train deep learning models from scratch, and pick up tricks and tools for achieving highly accurate results along the way. You’ll also learn to leverage freely available, state-of-the-art pre-trained models to save time and get your deep learning application up and running quickly

Find out how to use multiple GPUs to train neural networks and effectively parallelize\ntraining of deep neural networks using TensorFlow.

This course covers the area in image analysis and computer vision that deals with generative models. By the end of this course students will be able to implement a GAN model to generate new images from a set of training examples.