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Your accelerated MCSA: Machine Learning course will teach you skills in operationalising Microsoft Azure machine learning and Big Data with R Server and SQL R Services. You'll learn to process and analyse large data sets using R and use Azure cloud services to build and deploy intelligent solutions.
Your expert Microsoft Certified Trainer (MCT) will immerse you in the course. You will learn through Firebrand's unique Lecture | Lab | Review technique - helping you to build and retain knowledge faster than traditional training styles. You will develop practial skills relevant to real world application, getting hands-on with Microsoft R Server, SQL R Services, Azure Machine Learning, Cognitive Services and Bot Framework technologies.
You'll cover a range of big data, Microsoft R and cloud data science topics including:
During your 6-day accelerated MCSA course, you'll also be prepared for exams 70-773: Analyzing Big Data with Microsoft R and 70-774: Perform Cloud Data Science with Azure Machine Learning. You'll sit both exams at the Firebrand training centre during the course. Covered by your Certification Guarantee.
The MCSA Machine Learning certification is designed for those looking to demonstrate their expertise using R and Azure Machine Learning - best suited to data science or data analyst job roles. Achieving the MCSA certification will act as the first step to becoming a Data Management and Analytics Microsoft Certified Solutions Expert (MCSE).
Explain how Microsoft R Server and Microsoft R Client work.
Lessons
Lab : Exploring Microsoft R Server and Microsoft R Client
After completing this module, you’ll be able to:
At the end of this module the student will be able to use R Client with R Server to explore big data held in different data stores.
Lessons
Lab : Exploring Big Data
After completing this module, you’ll be able to:
Explain how to visualize data by using graphs and plots.
Lessons
Lab : Visualizing data
After completing this module, you’ll be able to:
Explain how to transform and clean big data sets.
Lessons
Lab : Processing big data
After completing this module, you’ll be able to:
Explain how to implement options for splitting analysis jobs into parallel tasks.
Lessons
Lab : Using rxExec and RevoPemaR to parallelise operations
After completing this module, you’ll be able to:
Explain how to build and evaluate regression models generated from big data
Lessons
Lab : Creating a linear regression model
After completing this module, you’ll be able to:
Explain how to create and score partitioning models generated from big data.
Lessons
Lab : Creating and evaluating partitioning models
After completing this module, you’ll be able to:
Explain how to transform and clean big data sets.
Lessons
Lab : Processing big data in SQL Server and Hadoop
After completing this module, you’ll be able to:
This module introduces machine learning and discussed how algorithms and languages are used.
Lessons
Lab : Introduction to machine Learning
After completing this module, you’ll be able to:
Describe the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio.
Lessons
Lab : Introduction to Azure machine learning
After completing this module, you’ll be able to:
At the end of this module the student will be able to upload and explore various types of data in Azure machine learning.
Lessons
Lab : Visualizing Data
After completing this module, you’ll be able to:
This module provides techniques to prepare datasets for use with Azure machine learning.
Lessons
Lab : Preparing data for use with Azure machine learning
After completing this module, you’ll be able to:
This module describes how to explore and use feature engineering and selection techniques on datasets that are to be used with Azure machine learning.
Lessons
Lab : Using feature engineering and selection
After completing this module, you’ll be able to:
This module describes how to use regression algorithms and neural networks with Azure machine learning.
Lessons
Lab : Building Azure machine learning models
After completing this module, you’ll be able to:
This module describes how to use classification and clustering algorithms with Azure machine learning.
Lessons
Lab : Using classification and clustering with Azure machine learning models
After completing this module, you’ll be able to:
This module describes how to use R and Python with azure machine learning and choose when to use a particular language.
Lessons
Lab : Using R and Python with Azure machine learning
After completing this module, you’ll be able to:
This module describes how to use hyper-parameters and multiple algorithms and models, and be able to score and evaluate models.
Lessons
Lab : Initialising and optimising machine learning models
After completing this module, you’ll be able to:
This module explores how to provide end users with Azure machine learning services, and how to share data generated from Azure machine learning models.
Lessons
Lab : Using Azure machine learning models
After completing this module, you’ll be able to:
This module introduces the cognitive services APIs for text and image processing to create a recommendation application, and describes the use of neural networks with Azure machine learning.
Lessons
Lab : Using Cognitive Services
After completing this module, you’ll be able to:
This module describes how use HDInsight with Azure machine learning.
Lessons
Lab : Machine Learning with HDInsight
After completing this module, you’ll be able to:
This module describes how to use R and R server with Azure machine learning, and explain how to deploy and configure SQL Server and support R services.
Lessons
Lab : Using R services with machine learning
After completing this module, you’ll be able to:
You will sit the following exams on-site, during the course. Covered by your Certification Guarantee.
Exam 70-773: Analyzing Big Data with Microsoft R
Technology: Microsoft R Server, SQL R Services
Languages: English
Skills measured:
Exam 70-774: Perform Cloud Data Science with Azure Machine Learning
Technology: Azure Machine Learning, Bot Framework, Cognitive Services
Languages: English
Skills measured:
Your accelerated course includes:
It is recommended you have the following prerequisite skills and knowledge before attending the course:
We interview all applicants for the course on their technical background, degrees and certifications held, and general suitability. If you get through this screening process, it means you stand a great chance of passing.
Firebrand Training is an immersive training environment. You must be committed to the course. The above prerequisites are guidelines, but many students with less experience have other background or traits that have enabled their success in accelerated training through Firebrand Training.
If you have any doubts as to whether you meet the pre-requisites please call (0)8 44 68 27 85 and speak to one of our enrolment consultants, who can help you with a training plan.
We've currently trained 134.561 students in 12 years. We asked them all to review our Accelerated Learning. Currently,
94,89% have said Firebrand exceeded their expectations:
"Great trainers and facilities, and being away from work & family really helps you focus on the material."
R.C.M., PGI - Protection Group International Ltd.. (22/4/2024 (Monday) to 27/4/2024 (Saturday))
"Excellent training. Material and instructor very good. Good facilities. Preferred method of learning moving forward."
J.B.. (22/4/2024 (Monday) to 27/4/2024 (Saturday))
"The in-person training is always effective. I have never done certification before I have around 20+ yrs of exp and I did my first one with Firebrand. The only reason I passed the exam due to focused time and no distraction."
Arul Jayaraman, Yayati consulting ltd. (22/4/2024 (Monday) to 27/4/2024 (Saturday))
"The course was very interesting and interactive."
Matthew Clulee, Venture Systems Group. (23/4/2024 (Tuesday) to 26/4/2024 (Friday))
"A good training course, with experienced and knowledgeable instructor, reacting to skills and experience level of the class students."
Mike Hilton, WWF-UK. (22/4/2024 (Monday) to 24/4/2024 (Wednesday))
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