Perform Cloud Data Science with Azure Machine Learning(20774)

Description

The main purpose of the course is to give students the ability to analyze and present data by using Azure Machine Learning, and to provide an introduction to the use of machine learning with big data tools such as HDInsight and R Services.

The primary audience for this course:

People who wish to analyze and present data by using Azure Machine Learning.
The secondary audience is IT professionals, Developers, and information workers who need to support solutions based on Azure machine learning.
At Course Completion

After completing this course, students will be able to:

Explain machine learning, and how algorithms and languages are used
Describe the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio
Upload and explore various types of data to Azure Machine Learning
Explore and use techniques to prepare datasets ready for use with Azure Machine Learning
Explore and use feature engineering and selection techniques on datasets that are to be used with Azure Machine Learning
Explore and use regression algorithms and neural networks with Azure Machine Learning
Explore and use classification and clustering algorithms with Azure Machine Learning
Use R and Python with Azure Machine Learning, and choose when to use a particular language
Explore and use hyperparameters and multiple algorithms and models, and be able to score and evaluate models
Explore how to provide end-users with Azure Machine Learning services, and how to share data generated from Azure Machine Learning models
Explore and use the Cognitive Services APIs for text and image processing, to create a recommendation application, and describe the use of neural networks with Azure Machine Learning
Explore and use HDInsight with Azure Machine Learning
Explore and use R and R Server with Azure Machine Learning, and explain how to deploy and configure SQL Server to support R services

Prerequisites

In addition to their professional experience, students who attend this course should have:

Programming experience using R, and familiarity with common R packages
Knowledge of common statistical methods and data analysis best practices.
Basic knowledge of the Microsoft Windows operating system and its core functionality.
Working knowledge of relational databases.

What’s included?

  • Authorized Courseware
  • Intensive Hands on Skills Development with an Experienced Subject Matter Expert
  • Hands on practice on real Servers and extended lab support 1.800.482.3172
  • Examination Vouchers & Onsite Certification Testing- (excluding Adobe and PMP Boot Camps)
  • Academy Code of Honor: Test Pass Guarantee
  • Optional: Package for Hotel Accommodations, Lunch and Transportation

With several convenient training delivery methods offered, The Academy makes getting the training you need easy. Whether you prefer to learn in a classroom or an online live learning virtual environment, training videos hosted online, and private group classes hosted at your site. We offer expert instruction to individuals, government agencies, non-profits, and corporations. Our live classes, on-sites, and online training videos all feature certified instructors who teach a detailed curriculum and share their expertise and insights with trainees. No matter how you prefer to receive the training, you can count on The Academy for an engaging and effective learning experience.

Methods

  • Instructor Led (the best training format we offer)
  • Live Online Classroom – Online Instructor Led
  • Self-Paced Video

Speak to an Admissions Representative for complete details

Curriculum

Module 1: Introduction to Machine Learning

This module introduces machine learning and discussed how algorithms and languages are used.

Lessons

What is machine learning?
Introduction to machine learning algorithms
Introduction to machine learning languages
Lab : Introduction to machine Learning

Sign up for Azure machine learning studio account
Run a simple experiment from gallery
Evaluate an experiment
After completing this module, students will be able to:

Describe machine learning
Describe machine learning algorithms
Describe machine learning languages
Module 2: Introduction to Azure Machine Learning

Describe the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio.

Lessons

Azure machine learning overview
Introduction to Azure machine learning studio
Developing and hosting Azure machine learning applications
Lab : Introduction to Azure machine learning

Explore the Azure machine learning studio workspace
Clone and run a simple experiment
Clone an experiment, make some simple changes, and run the experiment
After completing this module, students will be able to:

Describe Azure machine learning.
Use the Azure machine learning studio.
Describe the Azure machine learning platforms and environments.
Module 3: Managing Datasets

At the end of this module the student will be able to upload and explore various types of data in Azure machine learning.

Lessons

Categorizing your data
Importing data to Azure machine learning
Exploring and transforming data in Azure machine learning
Lab : Visualizing Data

Prepare Azure SQL database
Import data
Visualize data
Summarize data
After completing this module, students will be able to:

Understand the types of data they have.
Upload data from a number of different sources.
Explore the data that has been uploaded.
Module 4: Preparing Data for use with Azure Machine Learning

This module provides techniques to prepare datasets for use with Azure machine learning.

Lessons

Data pre-processing
Handling incomplete datasets
Lab : Preparing data for use with Azure machine learning

Explore some data using Power BI
Clean the data
After completing this module, students will be able to:

Pre-process data to clean and normalize it.
Handle incomplete datasets.
Module 5: Using Feature Engineering and Selection

This module describes how to explore and use feature engineering and selection techniques on datasets that are to be used with Azure machine learning.

Lessons

Using feature engineering
Using feature selection
Lab : Using feature engineering and selection

Merge datasets
Use PCA to reduce dimensions
Select some variables and edit metadata
After completing this module, students will be able to:

Use feature engineering to manipulate data.
Use feature selection.
Module 6: Building Azure Machine Learning Models

This module describes how to use regression algorithms and neural networks with Azure machine learning.

Lessons

Azure machine learning workflows
Scoring and evaluating models
Using regression algorithms
Using neural networks
Lab : Building Azure machine learning models

Using Azure machine learning studio modules for regression
Evaluate machine learning models
Add further regression models
Create and run a neural-network based application
After completing this module, students will be able to:

Describe machine learning workflows.
Explain scoring and evaluating models.
Describe regression algorithms.
Use a neural-network.
Module 7: Using Classification and Clustering with Azure machine learning models

This module describes how to use classification and clustering algorithms with Azure machine learning.

Lessons

Using classification algorithms
Clustering techniques
Selecting algorithms
Lab : Using classification and clustering with Azure machine learning models

Using Azure machine learning studio modules for classification.
Add k-means section to an experiment
Add PCA for anomaly detection.
Evaluate the models
After completing this module, students will be able to:

Use classification algorithms.
Describe clustering techniques.
Select appropriate algorithms.
Module 8: Using R and Python with Azure Machine Learning

This module describes how to use R and Python with azure machine learning and choose when to use a particular language.

Lessons

Using R
Using Python
Using Jupyter notebooks
Supporting R and Python
Lab : Using R and Python with Azure machine learning

Adding R and Python scripts
Using Python with Visual Studio IDE
Add a Jupyter notebook
Run Jupyter notebook
Import packages for R/Python
Data visualization using R/Python
R programming to work on a time series
After completing this module, students will be able to:

Explain the key features and benefits of R.
Explain the key features and benefits of Python.
Use Jupyter notebooks.
Support R and Python.
Module 9: Initializing and Optimizing Machine Learning Models

This module describes how to use hyper-parameters and multiple algorithms and models, and be able to score and evaluate models.

Lessons

Using hyper-parameters
Using multiple algorithms and models
Scoring and evaluating ensembles
Lab : Initializing and optimizing machine learning models

Using hyper-parameters
Build an ensemble using stacking
Evaluate the ensemble
After completing this module, students will be able to:

Use hyper-parameters.
Use multiple algorithms and models to create ensembles.
Score and evaluate ensembles.
Module 10: Using Azure Machine Learning Models

This module explores how to provide end users with Azure machine learning services, and how to share data generated from Azure machine learning models.

Lessons

Deploying and publishing models
Exporting data
Lab : Using Azure machine learning models

Deploy machine learning models
Consume a published model
Export data
Use exported data in machine learning model
After completing this module, students will be able to:

Deploy and publish models.
Export data to a variety of targets.
Module 11: Using Cognitive Services

This module introduces the cognitive services APIs for text and image processing to create a recommendation application, and describes the use of neural networks with Azure machine learning.

Lessons

Cognitive services overview
Processing text
Processing images
Creating recommendations
Lab : Using Cognitive Services

Create and run a text processing application
Create and run an image processing application
Create and run a recommendation application
After completing this module, students will be able to:

Describe cognitive services.
Process text through an application.
Process images through an application.
Create a recommendation application.
Module 12: Using Machine Learning with HDInsight

This module describes how use HDInsight with Azure machine learning.

Lessons

Introduction to HDInsight
HDInsight cluster types
HDInsight and machine learning models
Lab : Machine Learning with HDInsight

Deploy an HDInsight cluster
Use the HDInsight cluster
Display data in Power BI
After completing this module, students will be able to:

Describe the features and benefits of HDInsight.
Describe the different HDInsight cluster types.
Use HDInsight with machine learning models.
Module 13: Using R Services with Machine Learning

This module describes how to use R and R server with Azure machine learning, and explain how to deploy and configure SQL Server and support R services.

Lessons

R and R server overview
Using R server with machine learning
Using R with SQL Server
Lab : Using R services with machine learning

Deploy DSVM
Explore the data science VM
Configure R server
Run a sample R server application
Deploy a SQL server 2016 Azure VM
Configure SQL Server to allow execution of R scripts
Execute R scripts inside T-SQL statements
Use R to visualize data
After completing this module, students will be able to:

Implement interactive queries.
Perform exploratory data analysis.

Enrolled