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Analyzing Big Data with Microsoft R 70-773 Dumps Provided Study Notes

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Algorithms for grouping unlabeled Data.
HDinsights
Python Languages.
Machine Learning Projects and workspace
Azure Machine Learning.
Data Science
SQL/T-SQL
Microsoft R Servers.
Cognitive Services/API
Machine Learning models

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Reference: https://www.microsoft.com/en-us/learning/exam-70-774.aspx

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Perform Cloud Data Science with Azure Machine Learning 70-774 Exam

Perform Cloud Data Science with Azure Machine Learning 70-774 Exam is centered around Azure Machine Learning, Bot Framework, and Cognitive Services. The exam is designed to target candidates who are Data Scientists or Analysts using the Microsoft Azure cloud services to build and deploy intelligent solutions. This exam will test your understanding and familiarity with common data science processes such as filtering and transforming data sets, model estimation, and model evaluation. Machine Learning is one of the hottest topics and technologies in the industry right now.

Machine Learning is being used to combine with Big Data to automate the processes of gaining insights and predicting future behavior of systems, devices, and people. In an effort to expand out the breadth of certifications and exams offered around Microsoft Azure and the cloud, Microsoft has added the Perform Cloud Data Science with Azure Machine Learning Microsoft 70-774 Exam. This exam only available for the candidates till 30th June 2019 after that this may get replace with some other exam so you can plan get it completed before it get expire.

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This is the list of the contents in our Perform Cloud Data Science with Azure Machine Learning 70-774

Validate models
Import and Export Data to and from ML.
Perform data sciences at scale by using HD Insights
Explore and summarize data
Cleanse data for Azure Machine Learning
Azure Machine Learning models

Microsoft 070-774 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Use Other Services for Machine Learning	10%	- Integrate with HDInsight - Leverage Cortana Intelligence Gallery - Work with Microsoft Cognitive Toolkit - Use SQL Server R Services
Topic 2: Develop Machine Learning Models	35%	- Select appropriate algorithms 1. Unsupervised learning methods 2. Supervised learning methods - Train and tune models 1. Configure parameters 2. Split training and test sets - Validate and evaluate models 1. Perform cross-validation 2. Use accuracy, precision, recall, AUC
Topic 3: Operationalize and Manage Azure Machine Learning Services	25%	- Consume models and APIs 1. Use Cognitive Services APIs 2. Call deployed web services - Deploy models as web services 1. Configure endpoints and access 2. Publish to Azure ML Studio - Manage workspaces and experiments 1. Create and organize projects 2. Share and collaborate
Topic 4: Prepare Data for Analysis in Azure Machine Learning	30%	- Cleanse and transform data 1. Handle missing values and outliers 2. Remove duplicates and inconsistent entries - Explore and summarize data 1. Generate statistical summaries 2. Visualize data distributions - Perform feature engineering 1. Normalize and scale features 2. Create derived features - Import and export data 1. Import from files and external sources 2. Export results and datasets 3. Connect to Azure storage and databases

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