Explanation
In this course, you’ll gain hands-on experience with AWS SageMaker through hands-on labs that demonstrate unique concepts. We’ll start by setting up your SageMaker environment. If you are new to machine learning, you will learn how to handle complex data types, missing data, and how to verify model quality. These topics are essential for machine learning professionals and certification exams. SageMaker uses containers to package algorithms and frameworks, such as Pytorch and TensorFlow. The container-based approach provides a common interface for building and deploying your models, and it’s easy to convert your model into a production application. Through a series of brief labs, you’ll train, deploy, and call your first SageMaker model. Like any software project, a machine-learning solution also needs continuous improvement. We’ll look at how to safely incorporate new changes into a production system, perform A/B testing, and even revert changes when necessary, all with zero downtime on your application.
We will also discuss emerging social justice trends in machine learning and AI systems. What will you do if your users accuse your style of being racist or sexist? How will you manage? In this section, we will cover the concept of fairness, how to explain the decision made by the model, the different types of bias, and how to measure it. We will also cover cloud security and how to protect your data from unauthorized use. You’ll learn about advisor methods and how to add features like movies and product recommendations. The algorithms you learn in the course are the most sophisticated, and adjusting them to your data can be difficult. We’ll look at how to program your model with an automated tool, and you’ll gain experience with time series forecasting, anomaly detection, and building custom deep learning models.
What will you learn?
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You’ll get first-hand experience on how to train, configure, deploy, and integrate ML in the AWS cloud.
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Built-in AWS Algorithms, Bring your own, ready-to-use AI capabilities
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The Complete Guide to AWS Certified Machine Learning – Specialized (MLS-C01)
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Includes a high quality timed practice test (many courses charge a separate fee for the practice test)
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Model deployment from zero
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How to add and invoke ML from your application
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Direct Hyperparameter Tuning
Who is this course for?
- This course is for anyone interested in learning AWS-based machines and data science
- AWS Certified Machine Learning – Special Preparation
AWS Guidelines for Special Machine Learning Certification MLS-C01 (2023)
- Publisher: Udemy
- Teacher: Chandra Lingam
- Language : English
- Level : All Levels
- Number of Courses: 234
- Duration : 17 hours and 36 minutes
Content 2/2023
Requirements
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AWS Account – I’ll walk you through the steps to set one up
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Basic knowledge of Pandas, Numpy, Matplotlib
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Be an active student and use the course discussion forum if you need help – Please do not post product help required for course reviews.
Pictures
Sample Clip
Installation Guide
Extract files and watch your favorite player
Subtitle : English
Quality: 720p
Download Links
Password file: free download software
file size
4.72 GB