Azure Machine Learning training

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.Enroll & Get Certified now!

  • ✔ Course Duration : 32 hrs
  • ✔ Training Options : Live Online / Self-Paced / Classroom
  • ✔ Certification Pass : Guaranteed

Azure Machine Learning training

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.

What you will Learn

  • Master Data Science and Machine Learning Models using Azure ML.
  • Understand the concepts and intuition of Machine Learning algorithms
  • Build Machine Learning models within minutes
  • Choose the correct Machine Learning Algorithm using the cheatsheet
  • Deploy production grade Machine Learning algorithms
  • Deploy Machine Learning webservices in the simplest form possible
  • Bring in great value to business you manage

PREREQUISITES

  • Basic Math is good enough. This course does not require background in Data Science. Will be great if you have one.
  • Free or paid subscription to Microsoft Azure is required. It may ask for Phone and/or Credit Card for verification

CURRICULUM

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
  • View 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

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.

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 : Managing Datasets
  • 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.

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.

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
  • Prepare datasets
  • Use Join to Merge data
After completing this module, students will be able to:
  • Use feature engineering to manipulate data.
  • Use feature selection.

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
  • 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.

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.

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
  • Incorporating R and Python into Machine Learning experiments
Lab : Using R and Python with Azure machine learning
  • Exploring data using R
  • Analyzing data using Python
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.

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 Models
Lab : Initializing and optimizing machine learning models
  • Using hyper-parameters
After completing this module, students will be able to:
  • Use hyper-parameters.
  • Use multiple algorithms and models to create ensembles.
  • Score and evaluate ensembles.

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
  • Consuming Experiments
Lab : Using Azure machine learning models
  • Deploy machine learning models
  • Consume a published model
After completing this module, students will be able to:
  • Deploy and publish models.
  • Export data to a variety of targets.

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 language
  • Processing images and video
  • Recommending products
Lab : Using Cognitive Services
  • Build a language application
  • Build a face detection application
  • Build 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.

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
  • Provision an HDInsight cluster
  • Use the HDInsight cluster with MapReduce and Spark
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.

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
  • Prepare a sample SQL Server database and configure SQL Server and R
  • Use a remote R session
  • Execute R scripts inside T-SQL statements
After completing this module, students will be able to:
  • Implement interactive queries.
  • Perform exploratory data analysis.

FAQs

You can enroll for this classroom training online. Payments can be made using any of the following options and receipt of the same will be issued to the candidate automatically via email.
1. Online ,By deposit the mildain bank account
2. Pay by cash team training center location

Highly qualified and certified instructors with 20+ years of experience deliver more than 200+ classroom training.

Contact us using the form on the right of any page on the mildaintrainings website, or select the Live Chat link. Our customer service representatives will be able to give you more details.

You will never miss a lecture at Mildaintrainigs! You can choose either of the two options: View the recorded session of the class available in your LMS. You can attend the missed session, in any other live batch.

We have a limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.

Yes, you can cancel your enrollment if necessary prior to 3rd session i.e first two sessions will be for your evaluation. We will refund the full amount without deducting any fee for more details check our Refund Policy

Yes, the access to the course material will be available for lifetime once you have enrolled into the course.

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