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![Preprocessing Data • Clean Missing Values • Normalized-Losses column Remove • Remove any rows having missing data • Exclude normalized-loss[Use Project Columns] • Clean rows having missing data [ Clean Missing Data Module]](https://image.slidesharecdn.com/machinelearningandazuremlstudio-160206100821/75/Machine-learning-and-azure-ml-studio-11-2048.jpg)
![Apply Learning Algorithm • Classification or Regression ??? • Split Data to train and test • Train [0.75] and test[0.25] … Use split data, Run Experiment • Machine Learning -> Initialize Model -> Regression->Linear Regression • Train Model Module](https://image.slidesharecdn.com/machinelearningandazuremlstudio-160206100821/75/Machine-learning-and-azure-ml-studio-13-2048.jpg)
Uploaded byYogendra Tamang
Machine learning and azure ml studio
This document provides an overview of machine learning and Azure ML Studio. It discusses how machine learning grew out of artificial intelligence work and is used for applications like database mining, recommendations, and computer vision. The document then outlines the process for creating models on Azure ML Studio, including getting data, pre-processing, defining features, training a model with an algorithm like linear regression, scoring and testing the model. It provides an example of using the automobile price dataset to predict new automobile prices through this process and a demonstration.
Machine learning and azure ml studio
- 1. Machine Learning andAzure ML Studio Yogendra Tamang ASPNET Meetup 20 February 2016
- 2. Outline • Introduction • CreatingModels • Regression • Creating Models on Azure ML • Demo
- 3. Machine Learning ? •AI • Learning Algorithm • Lots of examples • Testing and Evaluation
- 4. Machine Learning • MachineLearning - Grew out of work in AI - New capability for computers • Examples: - Database mining • Large datasets from growth of automation/web. • E.g., Web click data, medical records, biology, engineering - Applications can’t program by hand. • E.g., Autonomous helicopter, handwriting recognition, most of Natural Language Processing (NLP), Computer Vision. - Self-customizing programs • E.g., Amazon, Netflix product recommendations - Understanding human learning (brain, real AI).
- 5. Machine Learning • Autonomous Helicopter •Autonomous Driving • Face Detection
- 6. Autonomous Cars, FacialDetection, NLP..
- 7. Azure ML • CreateModel • Get Data • Pre-processing of data • Define Features • Train the Model • Choose and apply learning algorith • Score and Test the model • Predict new automobile prices
- 8. Creating Models 1. Createnew Experiment 2. Type in automobile to see Automobile Price Dataset 1. Play Around with datasets 3. Pre-process Data
- 10. Getting data • AutomobilePrice Data • Each row for single automobile
- 11. Preprocessing Data • CleanMissing Values • Normalized-Losses column Remove • Remove any rows having missing data • Exclude normalized-loss[Use Project Columns] • Clean rows having missing data [ Clean Missing Data Module]
- 12. Defining Features • Requiresexperimentation and knowledge about context • Some feature better at predicting target. • Strong correlation with other features
- 13. Apply Learning Algorithm •Classification or Regression ??? • Split Data to train and test • Train [0.75] and test[0.25] … Use split data, Run Experiment • Machine Learning -> Initialize Model -> Regression->Linear Regression • Train Model Module
- 14. Training the model •Train Model Module • Left Port for Model, Right port for data • Run experiment
- 15. Predict New AutomobilePrices • Score Model • Left Port from Train model • Right Port from Test Data • Run • Evaluate Model
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