The main purpose of the course is to give you 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.
Your course package is designed to provide maximum learning and convenience. This is included in the price of your course:
Your expert instructor will get you ready for the following exam and certification, which are included in your course package and covered by the Certification guarantee.
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.
Using our engaging learning methodology including a variety of tools, we’ll cover the entire curriculum.
Module 1: Introduction to Machine Learning
This module introduces machine learning and discussed how algorithms and languages are used.
- 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
Module 2: Introduction to Azure Machine Learning
Describe the purpose of Azure Machine Learning, and list the main features of Azure Machine Learning Studio.
- 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
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.
- 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
Module 4: Preparing Data for use with Azure Machine Learning
This module provides techniques to prepare datasets for use with Azure machine learning.
- Data pre-processing
- Handling incomplete datasets
Lab : Preparing data for use with Azure machine learning
- Explore some data using Power BI
- Clean the data
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.
- Using feature engineering
- Using feature selection
Lab : Using feature engineering and selection
- Prepare datasets
- Use Join to Merge data
Module 6: Building Azure Machine Learning Models
This module describes how to use regression algorithms and neural networks with Azure machine learning.
- 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
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.
- 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
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.
- 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
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.
- Using hyper-parameters
- Using multiple algorithms and models
- Scoring and evaluating Models
Lab : Initializing and optimizing machine learning models
- Using hyper-parameters
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.
- Deploying and publishing models
- Consuming Experiments
Lab : Using Azure machine learning models
- Deploy machine learning models
- Consume a published model
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.
- 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
- Create a recommendation application.
Module 12: Using Machine Learning with HDInsight
This module describes how use HDInsight with Azure machine learning.
- 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
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.
- 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
The Virtual Classroom is an online room, where you will join your instructor and fellow classmates in real time. Everything happens live and you can interact freely, discuss, ask questions, and watch your instructor present on a whiteboard, discuss the courseware and slides, work with labs, and review.
Your prereading is available for you in your personal space at readynez.com: My Readynez. Simply log in, find your course and start your preparations.
Your exam voucher is usually included in your virtual training package. When you´re ready to sit your exam, you just book it with the exam provider. You can sit most exams from home or at a local test centre. We’re here to help you with that process.
Yes, you can sit exams from all the major Vendors like Microsoft, Cisco etc from the comfort of your home or office.
Your Readynez Course package includes the exam voucher for Microsoft exams, AWS exams, ISO Exams and almost every other exam.
With Readynez you do any course form the comfort of your home or office. Readynez provides support and best practices for your at-home classroom and you can enjoy learning with minimal impact on your day-to-day life. Plus you'll save the cost and the environmental burden of travelling.
Well, learning is limitless, when you are motivated, but you need the right path to achieve what you want. Readynez consultants have many years of experience customizing learner paths and we can design one for you too. We are always available with help and guidance, and you can reach us on the chat or write us at email@example.com.