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On this accelerated AWS Practical Data Science with Amazon SageMaker (PDSASM) course, you'll learn how to solve a real-world case with Machine Learning (ML) and produce actionable results using Amazon SageMaker.
In just 1 day, you’ll learn the fundamentals of the stages of a typical data science process for ML, from analysing and visualising a dataset, to preparing the data and feature engineering. You’ll also learn practical aspects of model building, training, tuning, and deployment with Amazon SageMaker. You’ll build knowledge on:
As an official AWS Training Partner, through Firebrand’s Lecture | Lab | Review methodology, you’ll get access to official courseware, learn from certified instructors, and train in a distraction-free environment.
This course is ideal for:
There is no exam for this accelerated course.
Your accelerated course includes:
Before attending this accelerated course, AWS recommend you have:
Unsure whether you meet the prerequisites? Don’t worry. Your training consultant will discuss your background with you to understand if this course is right for you.
Here's the Firebrand Training review section. Since 2001 we've trained exactly 134561 students and asked them all to review our Accelerated Learning. Currently, 94.69% have said Firebrand exceeded their expectations.
Read reviews from recent accelerated courses below or visit Firebrand Stories for written and video interviews from our alumni.
"Think twice, dont do it."
Anonymous (18.11.2019 (Monday) to 24.11.2019 (Sunday))
"If you have the brain, but not the time, Firebrand is the best for you."
Anonymous (26.8.2019 (Monday) to 29.8.2019 (Thursday))
"Excellent quality instruction; super intensive pace that will take you back 20 years to University exam cramming.
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Anonymous (20.5.2019 (Monday) to 23.5.2019 (Thursday))
"Brilliant course. Amazon AWS rules the cloud. A great platform for all companies and all requirements. Intense and worth it. Give us great possible..."
Anonymous, Digiprot (20.5.2019 (Monday) to 26.5.2019 (Sunday))
"Fast track to certification."
Anonymous (25.2.2019 (Monday) to 2.3.2019 (Saturday))
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26.8.2024 (Monday) |
27.8.2024 (Tuesday) |
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10.2.2025 (Monday) |
11.2.2025 (Tuesday) |
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24.3.2025 (Monday) |
25.3.2025 (Tuesday) |
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5.5.2025 (Monday) |
6.5.2025 (Tuesday) |
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16.6.2025 (Monday) |
17.6.2025 (Tuesday) |
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