AWS SageMaker Machine Learning Engineer in 30 Days + ChatGPT
AWS SageMaker Machine Learning Engineer in 30 Days + ChatGPT Course
Build 30+ ML Projects in 30 Days in AWS, Master SageMaker JumpStart, Canvas, AutoPilot, DataWrangler, Lambda & S3
This Udemy AWS SageMaker Machine Learning Engineer in 30 Days + ChatGPT Course created by Dr. Ryan Ahmed with 43 hours on-demand video, 37 articles, 29 downloadable resources and Certificate of completion. This course is unique and exceptional in many ways, it includes several practice opportunities, quizzes, and final capstone projects. In this course, students will learn how to create production-level ML models using AWS.
What you’ll learn
- Build, Train, Test and Deploy Machine Learning Models in AWS
- Learn SageMaker Built-in Algorithms such as Linear Learner, XG-Boost, Principal Component Analysis (PCA), and K-Nearest Neighbors
- Define and Perform Image and Text Labeling Jobs Using AWS SageMaker GroundTruth
- Prepare, Clean and Visualize data Using AWS SageMaker Data Wrangler without Writing any Code
- Optimize ML model hyperparameters using GridSearch, Bayesian & Random Search Optimization Techniques
- Master Key AWS services such as Simple Storage Service (S3), Elastic Compute Cloud (EC2),
- Identity and Access Management (IAM) and CloudWatch
- Understand Machine Learning workflow automation using AWS Lambda, Step functions and SageMaker Pipelines.
- Learn how to define a lambda function in AWS management console, understand the anatomy of
- Lambda functions, and how to configure a test event in Lambda
- Train a Machine Learning Regression and Classifier Models Using No-code AWS Canvas
- Learn how to leverage Amazon SageMaker Autopilot and SageMaker Canvas to train multiple models without writing any code.
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