This course is an entry point into the world of AI using Microsoft’s cloud-based solutions, such as Azure Machine Learning and Azure Cognitive Services. You will have the chance to learn and experience firsthand how to train and deliver machine learning models and use Azure Cognitive Services for typical AI workloads such as Computer Vision, Natural Language Processing and Conversational AI.
* Photos and promotional materials on this page are copyrighted on udacity.com
We only endorse high-quality online courses and educational content. This page contains affiliate links and we may earn a small commission when you click on the link at no additional cost to you. Thank you!
In this program, students will enhance their skills by building and deploying sophisticated machine learning solutions using popular open-source tools and frameworks, and gain practical experience running complex machine learning tasks using the built-in Azure labs accessible inside the Udacity classroom.
Learn to build, evaluate, and integrate predictive models that have the power to transform patient outcomes. Begin by classifying and segmenting 2D and 3D medical images to augment diagnosis and then move on to modeling patient outcomes with electronic health records to optimize clinical trial testing decisions. Finally, build an algorithm that uses data collected from wearable devices to estimate the wearer’s pulse rate in the presence of motion.
Leverage the Intel® Distribution of OpenVINO™ Toolkit to fast-track development of high-performance computer vision and deep learning inference applications, and run pre-trained deep learning models for computer vision on-premise. You will identify key hardware specifications of various hardware types (CPU, VPU, FPGA, and Integrated GPU), and utilize the Intel® DevCloud for the Edge to test model performance on the various hardware types. Finally, you will use software tools to optimize deep learning models to improve the performance of Edge AI systems.
Learn advanced machine learning techniques and algorithms and how to package and deploy your models to a production environment. Gain practical experience using Amazon SageMaker to deploy trained models to a web application and evaluate the performance of your models. A/B test models and learn how to update the models as you gather more data, an important skill in industry.