Microsoft MCSA: Machine Learning

HAUPTMERKMALE

The main purpose of the course is to give students the ability to use Microsoft R Server to create and run an analysis on a large dataset, and show how to utilize it in Big Data environments, such as a Hadoop or Spark cluster, or a SQL Server database.

Inhalt

Your course package is designed to provide maximum learning and convenience. This is included in the price of your course:

• VORBEREITUNG
• VON EINEM AUSBILDER GELEITETE SCHULUNG
• ALLE KURSUNTERLAGEN
• PRÜFUNGSGUTSCHEIN
• ZERTIFIZIERUNGSGARANTIE
• VORBEREITUNG
• VON EINEM AUSBILDER GELEITETE SCHULUNG
• UNTERKUNFT
• ALLE KURSUNTERLAGEN
• ALLE MAHLZEITEN
• PRÜFUNGSGUTSCHEIN
• ZERTIFIZIERUNGSGARANTIE

PRÜFUNGEN & ZERTIFIZIERUNGEN

Your expert instructor will get you ready for the following exams and certifications, which are included in your course package and covered by the Certification guarantee.

• Perform Cloud Data Science with Azure Machine Lear (70-774)
• Analyzing Big Data with Microsoft R (70-773)
• Microsoft MCSA: Machine Learning
• Microsoft Certified Professional (MCP)

VORAUSSETZUNGEN

You´ll have the perfect starting point for your training with these prerequisites:

- 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.

Vollständiger Lehrplan

Using our engaging learning methodology including a variety of tools, we’ll cover the entire curriculum.

Course 20774A Perform Cloud Data Science with Azure Machine Learning

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

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

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

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

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

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

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

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

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

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

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

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

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

Course 20773A: Analyzing Big Data with Microsoft R

Module 1: Microsoft R Server and R Client
Explain how Microsoft R Server and Microsoft R Client work.

- What is Microsoft R server
- Using Microsoft R client
- The ScaleR functions

Module 2: Exploring Big Data
At the end of this module the student will be able to use R Client with R Server to explore big data held in different data stores.

- Understanding ScaleR data sources
- Reading data into an XDF object
- Summarizing data in an XDF object

Module 3: Visualizing Big Data
Explain how to visualize data by using graphs and plots.

- Visualizing In-memory data
- Visualizing big data

Module 4: Processing Big Data
Explain how to transform and clean big data sets.

- Transforming Big Data
- Managing datasets

Module 5: Parallelizing Analysis Operations
Explain how to implement options for splitting analysis jobs into parallel tasks.

- Using the RxLocalParallel compute context with rxExec
- Using the revoPemaR package

Module 6: Creating and Evaluating Regression Models
Explain how to build and evaluate regression models generated from big data

- Clustering Big Data
- Generating regression models and making predictions

Module 7: Creating and Evaluating Partitioning Models
Explain how to create and score partitioning models generated from big data.

- Creating partitioning models based on decision trees.
- Test partitioning models by making and comparing predictions

Module 8: Processing Big Data in SQL Server and Hadoop
Explain how to transform and clean big data sets.

- Using R in SQL Server
- Using Hadoop Map/Reduce
- Using Hadoop Spark