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Machine Learning on AWS – 4 Day Training Session
Course Details:
Class Start Date:
Tuesday 4/18/2023
Class End Date:
Friday 4/21/2023
Cost per Student:
$ 1850
AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This course is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the lessons, you’ll gain insights into how these algorithms can be trained, tuned, and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the course will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few lessons, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics and predictive modeling through to sentiment analysis. By the end of this course, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS.
Working in a hands-on learning environment, led by our Machine Learning on AWS instructor, students will learn about and explore:
Please see Modules below
Machine Learning on AWS – 4 Day Training Session
Live Online Instructor Led Training
Tuesday 4.18.2023 – Friday 4.21.2023
08:30am EST – 4:30pm EST Daily
Cost per Student – $1850
AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This course is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the lessons, you’ll gain insights into how these algorithms can be trained, tuned, and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the course will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few lessons, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics and predictive modeling through to sentiment analysis. By the end of this course, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS.