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On this accelerated Implementing a Machine Learning Solution with Microsoft Azure Databricks course, you'll learn how to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning.
In just 2 days, you’ll build knowledge on deploying models trained on Azure Databricks to Azure Machine Learning, as well as:
As a Microsoft Gold Partner for Learning and through Firebrand’s Lecture | Lab | Review methodology, you’ll get access to Microsoft Official Curriculum (MOCs), learn from Microsoft Certified Trainers (MCTs), and train in a distraction-free environment.
This course is ideal for Data Scientists with experience of Pythion who need to learn how to apply their data science and machine learning skills on Azure Databricks.
Module 1: Introduction to Azure Databricks
Module 2: Training and Evaluating Machine Learning Models
Module 3: Managing Experiments and Models
Module 4: Integrating Azure Databricks and Azure Machine Learning
There is currently no exam for this accelerated course.
Your accelerated course includes:
Before attending this accelerated course, you should 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, 96.46% 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.
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Anonymous (3.9.2018 (Monday) to 5.9.2018 (Wednesday))
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Adela Toma, Bearing Point. (19.3.2018 (Monday) to 24.3.2018 (Saturday))
"Perfect training!"
P. E.. (4.12.2017 (Monday) to 6.12.2017 (Wednesday))
"A high tempo and lots to learn from a great instructor. Thanks for helping me pass the exam in a quick tempo."
Seth Lindholm, Advitum AB (SWEDEN). (21.11.2016 (Monday) to 25.11.2016 (Friday))
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21.8.2023 (Monday) |
22.8.2023 (Tuesday) |
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5.2.2024 (Monday) |
6.2.2024 (Tuesday) |
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18.3.2024 (Monday) |
19.3.2024 (Tuesday) |
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29.4.2024 (Monday) |
30.4.2024 (Tuesday) |
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10.6.2024 (Monday) |
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