AZURE MACHINE LEARNINGYogendra Tamang

Tech Mela 2016

29th May

ABOUT ME…

• Name :- Yogendra Tamang

• Education :-

• M. Sc. Eng. (CSKE), IOE-Pulchowk Campus

• Interests :- Machine Learning and Data Science, Web Development

• Experience :- 2+ years in .NET Technology

• Email:-

yogendra_tamang@outlook.com

48yogen@gmail.com

OUTLINE

• Background

• Getting Started

• Pre-processing and Cleaning Data

• Training Models

• Creating and Deploying Web Services

• Further Readings!

MACHINE LEARNING ???

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MACHINE LEARNING ???

• Learning ???

• Proper Weightage Assignment

• Single Universal Learning Algorithm.

INTUITIONS…

• How would you program

• a system that correctly classify digits from given image of digit ?

• Car for autonomous driving ?

• Recommender System ?

• Cancer Malignant or Not ?

• Image Classification

• Feature Engineering

MACHINE LEARNING

• Performance over time Increases

• No Explicit Programming

• Learning

• Supervised : Regression and Classification

• Unsupervised : Clustering

• Error Minimization : Optimization

MACHINE LEARNING OVER CLOUD

• Access to Advanced learning algorithm with minimal effort

• 'feed in data, get answer back‘ Approach

• Players

• Google Prediction API

• Amazon Machine Learning

• Azure Machine Learning

• BigML

• IBM Watson

MACHINE LEARNING PROBLEMS

• Spam Filtering

• Autonomous Driving

• Grouping News

• Anomaly Detection

• Classification, Regression

• Clustering

• Recommender Systems

AZURE MACHINE LEARNING

• Launched in Feb, 2015

• Complex Data Work Flow, using ready to use set of modules

• Incredible service for data scientists.

• Rich offerings

• Azure Market Place

WHY AZURE ML ?

• Data conversion and transformation.

• Feature Selection (Filter-based, Fisher LDA, etc).

• OpenCV algorithms (Image Reader and Cascade Image Classification).

• Python scripts (the script must contain an azureml_main function and eventually import zipped libraries).

• R models and scripts.

• Statistical Functions (correlation, trigonometric transformations, rounding, deviation, kurtosis, etc).

• Text Analytics (Feature Hashing, Named Entity Recognition, etc).

GETTING STARTED

• Getting Started

• Familiarization with Environment

• Workspace – Your Garage!!!

• Getting and Saving Data [Data Ingress]

• Train, Score and Evaluate Model

• Deploy Model

ITEMS YOU WORK WITH !!!

• Datasets

• Data Containers

• Experiments

• Set of connected components used to create, train, score and test the model.

• Modules

• Trained Models

• Web Services

READING DATA

• CSV (with or without header row)

• TSV (without or without header row)

• Plain Text (.txt)

• ZIP (.zip)

• SvmLight (more info here)

• ARFF (Attribute Relation File Format)

• RData (R Object or Workspace)

READING DATA

• Reader Module

• Web URL via HTTP

• Hive Query

• Azure SQL Database

• Azure Table

• Azure Blob Storage

• Data Feed Provider

• Descriptive Statistics Module

• See structure of your dataset

• YOUR FURTHER STEPS ARE DETERMINED !!!!

PRE-PROCESSING AND CLEANING DATA

• Reading Data

• Convert to Dataset

• Dealing With Missing Values

• Remove Row

• Replace with Mean/Median/Mode

• Project Column Module

• remove columns

• Metadata Editor

• Categorical features Conversion

• Apply Math Operator

MODULES

• Normalize data

• Join

• Split

• Execute R Script

• Execute Python Script

TRAINING A MODEL

• Select Algorithm [Model Config. ]

• Classification, Trees, Neural Networks,

• Clustering

• Split Data

• Train Data & Test Data [70/30, 80/20]

• To Ensure the model works well with new unseen data

• Train Model Object [ one from algorithm and other from training data]

TRAINING A MODEL

• Score model

• See scored label for the data

• Evaluate model

• Visualize matrix

• Confusion Matrix

• AuC

CREATING WEB-SERVICE

• Gets you and API

• Easy with Single Click !!!

DEPLOYING PREDICTIVE MODEL AS WEB SERVICE

• Publish as web service

• API Key

• C#, Python, R

• Monitor Web Services

• Access from Excel, Web Apps

READINGS

• https://azure.microsoft.com/en-us/documentation/services/machine-learning/

• http://cloudacademy.com/blog/azure-machine-learning/

• https://blog.onliquid.com/machine-learning-service-benchmark/

• https://gallery.cortanaintelligence.com

• http://robusttechhouse.com/microsoft-azure-machine-learning-review/

FURTHER RESOURCES

• Neural Nets in Azure Machine Learning (Net#)

• Azure Machine Learning Gallery [Now. Cortana Intelligence Gallery]

• For Pricing:

• https://azure.microsoft.com/en-us/pricing/details/machine-learning/

THANK YOU !!!

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