AWS SageMaker Practical for Beginners | Build 6 Projects

Partner: Udemy
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Description: # Update 22/04/2021 - Added a new case study on AWS SageMaker Autopilot. # Update 23/04/2021 - Updated code scripts and addressed Q&A bugs. Machine and deep learning are the hottest topics in tech! Diverse fields have adopted ML and DL techniques, from banking to healthcare, transportation to technology.AWS is one of the most widely used ML cloud computing platforms worldwide – several Fortune 500 companies depend on AWS for their business operations.SageMaker is a fully managed service within AWS that allows data scientists and AI practitioners to train, test, and deploy AI/ML models quickly and efficiently.In this course, students will learn how to create AI/ML models using AWS SageMaker. Projects will cover various topics from business, healthcare, and Tech. In this course, students will be able to master many topics in a practical way such as: (1) Data Engineering and Feature Engineering, (2) AI/ML Models selection, (3) Appropriate AWS SageMaker Algorithm selection to solve business problem, (4) AI/ML models building, training, and deployment, (5) Model optimization and Hyper-parameters tuning.The course covers many topics such as data engineering, AWS services and algorithms, and machine/deep learning basics in a practical way:Data engineering: Data types, key python libraries (pandas, Numpy, scikit Learn, MatplotLib, and Seaborn), data distributions and feature engineering (imputation, binning, encoding, and normalization).AWS services and algorithms: Amazon SageMaker, Linear Learner (Regression/Classification), Amazon S3 Storage services, gradient boosted trees (XGBoost), image classification, principal component analysis (PCA), SageMaker Studio and AutoML.Machine and deep learning basics: Types of artificial neural networks (ANNs) such as feed
Category: Development > Data Science > Amazon Sagemaker
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Price: 199.99
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Source: Impact
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