Azure ML Studio Training

Azure Machine Learning Studio is the development environment for developing data experiments.

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Duration: 2 Days

Course fee:$399.00 (₹0)

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Product Description

Azure Machine Learning basically deals with the information about the installation and performing some configuration on Cloud Computing Platform. Azure Machine Learning Studio is the software platform for performing Cloud Computing Infrastructure for the Machine Learning Application. The training primarily intends to deal with Exploration of Data and performing Data Visualization in detail. The concept of Data which are incoming and outgoing are demonstrated with some practical information for the aspirants or developers working on it. Many of Data Regression, Data Analytics and Report Generation is the leading factor making the Machine Learning Application more reliable. Azure Machine Learning Studio Online Training will allow the aspirants to learn the building of the predictive models on an early basis in very intuitive way. Basically Azure Training start with the overview of the Azure Machine Learning and then basics of Machine Learning Studio. With the help of Azure Machine Learning Studio you can build, test and deploy solutions on your data.

Objectives

  • Use Azure ML Studio for Visualizing and pre-processing of Data
  • Build Models and creating Predictions using Data Classification, Regression and Clustering Algorithms
  • Building Recommender System

Advantages of Azure ML Studio Training

  • Integration of R and Python code in the Model of ML Studio
  • Deploying predictive Solutions as Web Service API
  • Perfect Visualization and Pre-Processing of Data

Additional Information

Day 1

1. Basic Overview

– Introduction to Predictive Analysis
– Concept of Machine Learning
– Overview of Azure Machine Learning

2. ML Studio Inside Out

– Basics of ML Studio
– Introduction to Microsoft Azure
– Details on ML Studio
– Workspace as a Collaborative Environment

3. Data Exploration and Visualization

– Basic Concepts
    – Mean and Median
    – Standard Deviation and Variance
    – Understanding Histogram
    – Box and Whisker Plot
    – Scatter Plot
– Data Exploration in ML Studio

4. Working with Data In and Data Out of ML Studio

– Getting Data in ML Studio
    – Data Upload from PC
    – Enter Data Module
    – Data Reader Module
– Data Format Conversion
– Getting Data from ML Studio
    – Saving Dataset on PC

5. Data Preparation

– Data Manipulation
– Data Splitting Techniques
– Apply SQL Transformation Module
– Advanced Data Preprocessing

6. Regression Models

– Understanding Regression Algorithms
– Train, Score and Evaluate
– Linear Regression

Day 2

1. Classification Models

– Introducing Classification
– Training, Scoring and Evaluating Modules
– Multiclass Classification
– Multiclass Classification with Iris Dataset
– Multiclass Classification with Wine Dataset

2. Clustering

– Introducing K-means Clustering Algorithm
– Creation of K-means clustering Model using ML Studio
– Difference between Clustering and Classification

3. Understanding Recommender System

– Matchbox Recommender
– Building Recommendation System

4. R and Python Extension

– Basics of R
– Basics of Python
– Extending experiments using the Python Language
– Extending experiments using the R Language

5. Publishing Model as a Service

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