Recently amazon Launched an e-commerce company that launched Free AI courses And Got a free Certificate Machine learning (ML) has revolutionized various industries, providing the ability to extract valuable insights from data and make informed decisions. However, traditional ML approaches often require extensive technical expertise, limiting accessibility for many individuals.
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About the Amazon
It was founded by Jeff Bezos from his garage in Bellevue, Washington, on July 5, 1994. Initially an online marketplace for books, it has expanded into a multitude of product categories, a strategy that has earned it the moniker The Everything Store. It has multiple subsidiaries including Web Services (cloud computing), Zoox (autonomous vehicles), Kuiper Systems (satellite Internet), and Lab126 (computer hardware R&D). Its other subsidiaries include Ring, Twitch, IMDb, and Whole Foods Market. Its acquisition of Whole Foods in August 2017 for US$13.4 billion substantially increased its footprint as a physical retailer.
About the Free AI Courses Program
With SageMaker Data Wrangler and SageMaker Autopilot, data and research analysts can prepare data, train, and deploy machine learning (ML) models with minimal coding. You will learn to build ML models for tabular and time series data without deep knowledge of ML. You will also review the best practices for using SageMaker Data Wrangler and SageMaker Autopilot.
After completing this course, you will be able to build ML models to support proofs of concept (POCs). You will also be able to assist data scientists with potential ML model candidates to solve business problems.
• Course level: Intermediate
• Duration: 4 hours
Activities
This course includes eLearning interactions and knowledge checks.
Course objectives
In this course, you will learn to:
• Describe ML concepts and life cycle phases
• Describe metrics used for evaluating model candidates
• Use SageMaker Data Wrangler to prepare tabular and time series data for training an ML model
• Use SageMaker Autopilot to automatically build ML models and identify the best model from a list of model candidates based on your objective metric
• Describe best practices for using SageMaker Data Wrangler and SageMaker Autopilot
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Intended audience
This course is intended for:
• Data analysts
• Researchers from non-ML domains
• Operations research analysts
• Junior data scientists
Prerequisites
We recommend that attendees of this course have:
• Experience with analysis, cleansing, and transforming tabular or time series data
• Basic understanding of statistical measures and regression
• AWS Technical Essentials
Course outline
Module 1: Introduction to Machine Learning
ML Introduction
• ML Basics
• Problems ML Can Solve
• ML Life Cycle
• Challenges in Processing Data and Deriving Insights
• Knowledge Check
Model Building and Evaluation Metrics
• Introduction to Model Building
• Applying Evaluation Metrics to Select a Model
• Building an ML Model
Wrap Up
• Knowledge Check
• Conclusion
Module 2: Exploratory Data Analysis and Data Preparation
Introduction to SageMaker Data Wrangler
• SageMaker Data Wrangler
• Data Analysis
Data Preparation
• Quick Model
• Transforming Data
• Developing and Scaling Data Transformations
Wrap Up
• Knowledge Check
• Conclusion
Module 3: Deep Dive on SageMaker Autopilot
• Introduction to SageMaker Autopilot
• Datasets, Problem Types, and Training Modes
• Validation and Metrics
• Automatic Model Deployment
Wrap Up
• Knowledge Check
• Conclusion
Module 4: Operational Best Practices
Best Practices for SageMaker Data Wrangler
• Environmental Optimization
• Cost Optimization
• Data Optimization
• Security Optimization
Best Practices for SageMaker Autopilot
• Best Practices and Recommendations
Wrap Up
• Knowledge Check
• Conclusion
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How To Apply?
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