Along with good working experience and knowledge of how to train and evaluate models, you need to have a good understanding of all the ML algorithms provided by AWS. This course will teach you the use cases of built-in algorithms provided by AWS.
Being the front runner when it comes to cloud infrastructure, AWS has cutting edge services when it comes to machine learning. In this course, Modeling with AWS Machine Learning, you’ll learn to convert your data to an optimal model leveraging AWS SageMaker. First, you’ll explore supervised and unsupervised learning algorithms that are built-in to your AWS account and learn how to apply them to a specific business problem. Next, you’ll discover deep learning neural networks architecture and the built-in algorithms provided by AWS that cater specifically to computer vision and language processing domain. Finally, you’ll learn how to train a model on a SageMaker notebook, evaluate the model against the objective metric, and fine-tune the hyperparameters and arrive at an optimally performing model. When you’re finished with this course, you’ll have the skills and knowledge of all the AWS built-in algorithms and train, evaluate, and tune your models that are needed to master AWS SageMaker and clear AWS Machine Learning Specialty certification exam.
Course Overview [Autogenerated] Hi, everyone. My name is Sarah Vaughan and Dan Tapani on Welcome to My code on Modeling with AWS Machine Learning on an architect Working and Travel Domain focused on middleware on cloud technologies When it comes to machine learning, AWS Sage maker has been the industry leader, offering many services that complements the entire lifecycle from data preparation to model deployment. In this course, you will learn how to map a business problem to a machine learning problem, understand convolution and recommend neural networks on the built in algorithms offered by sage maker Pluralsight Sage Maker Notebook. Instance download on prepare banking dataset that will be used in the training process. Create an estimator object on monitor the training run in a sage maker console and finally, leverage sage makers automated, hyper parameter tuning to determine optimal hyper parameter values that feels the best performance metrics. By the end of this course, you will know all the features on benefits offered by AWS sage maker and how to train you evaluate on tune mission learning models before beginning this course, you should be familiar with basic python and using Jupyter notebooks. I hope you will join me on this journey to learn about developing models in AWS. Sage maker Been modeling with AWS Machine Learning Course at Pluralsight