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Introduction to Machine Learning on Azure
This document provides an introduction and overview of machine learning concepts and Azure Machine Learning. It defines machine learning as finding patterns in data and using those patterns to predict the future. It outlines the machine learning workflow and lifecycle, including preparing data, applying algorithms to find patterns, iterating to create the best model, and deploying the final model. It also describes machine learning concepts like supervised and unsupervised learning, and different problem types like regression, classification, and clustering. Finally, it discusses options for using Azure Machine Learning, including free and full-featured paid accounts, and demonstrates its use.
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Introduction to Machine Learning on Azure
- 2. Photo Credits :Konstantinos Poulakos Introduction to Machine Learning on Azure Athens Mar 17, 2017
- 3. PresenterInfo 1982 I startedworking with computers 1988 I started my professional career in computers industry. 1996 I started working with SQL Server 6.0 1998 I earned my first certification at Microsoft as Microsoft Certified Solution Developer (3rd in Greece) I started my career as Microsoft Certified Trainer (MCT) with more than 30.000 hours of training until now! 2010 I became for first time Microsoft MVP on Data Platform I created the SQL School Greece www.sqlschool.gr 2012 I became MCT Regional Lead by Microsoft Learning Program. 2013 I was certified as MCSE : Data Platform & MCSE : Business Intelligence 2016 I was certified as MCSE: Data Management & Analytics Antonios Chatzipavlis SQL Server Expert & Evangelist MCT, MCSE, MCITP, MCPD, MCSD, MCDBA, MCSA, MCTS, MCAD, MCP, OCA, ITIL-F
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- 7. PresentationContent ▪ What isMachine Learning? ▪ Machine Learning Concepts ▪ Azure Machine Learning
- 8. What is MachineLearning?
- 9. Finds patterns indata Uses those patterns to predict the future What is Machine Learning?
- 10. Machine Learning Workflow DataModel Contains Patterns Finds Patterns Recognizes Patterns Application Supplies new data to see if it matches known patterns
- 11. ▪ Asking theRight Question ▪ Choosing what question to ask is the most important part of making Machine Learning ▪ Ask yourself ▪ Do you have the right data to answer this question? ▪ Do you know how you will measure success? Start with Machine Learning
- 12. The Machine LearningLifecycle Raw Data Raw Data Apply Pre- processing Modules Prepared Data Apply Machine Learning Algorithms Iterate until data is ready Candidate Model Iterate to find the best model Deploy Model Chosen Model Applications Applications Re-create model regularly
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- 14. ▪ Training Data ▪The prepared data used to create a model ▪ Supervised Learning ▪ The value you want to predict is in the training data ▪ The data is labeled ▪ The most common ▪ Unsupervised Learning ▪ The value you want to predict in not in the training data ▪ The data is unlabeled Terminology
- 15. Categorizing Machine LearningProblems Regression Classification Clustering
- 16. ▪ Training data ▪Choose features ▪ Input training data (70%) ▪ Choose Learning Algorithm ▪ Generate Candidate model ▪ Testing a Model ▪ Input test data (30%) ▪ Generate target values from test data ▪ Compare target values generated from test data with actual target data Training and Testing Model
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- 18. Azure Machine LearningTrial Free Limited to Azure Machine Learning Features https://studio.azureml.net/home Add Machine Learning to Azure account Full Azure Integration https://portal.azure.com Azure Machine Learning Account Options
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