AWS: Introduction to Machine Learning on AWS
|
This course is intended for software developers and engineers taking their first steps with the AWS services that do much of heavy lifting of Machine Learning for you.
About this course
In this course, we start with some services where the training model and raw inference is handled for you by Amazon. We’ll cover services which do the heavy lifting of computer vision, data extraction and analysis, language processing, speech recognition, translation, ML model training and virtual agents. You’ll think of your current solutions and see where you can improve these solutions using AI, ML or Deep Learning. All of these solutions can work with your current applications to make some improvements in your user experience or the business needs of your application.
At a Glance:
Institution: AWS
Subject: Computer Science
Level: Introductory
Prerequisites:
Prior application development experience
Language: English
Video Transcript: English
Associated skills:Computer Vision, Amazon Web Services, Image Analysis, Cloud Computing, Deep Learning, Amazon Textract, Artificial Intelligence, Natural Language Processing, Machine Learning, Amazon Rekognition
User Reviews
Be the first to review “AWS: Introduction to Machine Learning on AWS”
Related Products
Microsoft Azure Cognitive Services: Face API
Machine learning and artificial intelligence are enabling great change today. This course will introduce you to the Microsoft Azure Cognitive Services Face API, helping you understand how to incorporate its functionality into your own apps.
UMontrealX: Machine Learning Use Cases in Finance
In the last six years, the financial sector has seen an increase in the use of machine learning models in financial, banking and insurance contexts. Data science and advanced analytics teams in the financial and insurance community are implementing these models regularly and have found a place for them in their toolbox.
HarvardX: Deploying TinyML
Learn to program in TensorFlow Lite for microcontrollers so that you can write the code, and deploy your model to your very own tiny microcontroller. Before you know it, you’ll be implementing an entire TinyML application.
IBM: Computer Vision and Image Processing Fundamentals.
Learn about computer vision, one of the most exciting fields in machine learning. artificial intelligence and computer science.
Image Augmentation: A Practical Guide to Prevent Overfitting in Computer Vision
This talk starts with demos of the basic standard transforms using the albumentations Python package, and work up to some more advanced strategies like CutMix and mixup.
Computer Vision Fundamentals with Google Cloud
This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases.
There are no reviews yet.