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

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

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Microsoft 070-774 Exam Syllabus Topics:

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