MCSA: Machine Learning - Feva Works IT Education Centre

MCSA: Machine Learning MCSAMACHINEL



理解數據背後的價值,透過分析處理、發掘蘊含的商業價值。

Big Data 大數據在全球掀起熱潮,許多知名網路科技包含 Google、Facebook、Apple 及 Netflix、都正急迫地找尋數據科學家。

無論你在什麼專業領域,學會拆解海量數據轉化成 SmartData,即能提供更專業服務、引領自己向大數據商機!

Azure Machine Learning 是特別強大的預測性分析方式:您可以使用現成的演算法程式庫;在連線到網際網路的電腦上建立模型,而不需要購買額外的設備或基礎結構;以及快速地部署預測解決方案。

Azure Machine Learning 不僅提供可建立預測性分析模型的工具,也提供完全受管理的服務,您可以透過這項服務將預測性模型部署為可供取用的 Web 服務。

Azure Machine Learning 提供可在雲端上建立完整預測性分析解決方案的工具:您可以快速地建立、測試、操作及管理預測模型。您不需要購買任何硬體,或手動管理虛擬機器。

MCSA: Machine Learning

立即考取 MCSA: Machine Learning 國際認證,並在機器學習,數據科學和分析方面建立您的未來!此認證證明你擁有操作 Microsoft Azure Machine Learning 和使用 R Server SQL R服務的大數據方面的專業知識。

Feva Works MCSA: Machine Learning 認證班系列課程,由微軟認證 MCT 講師精心規劃、授課,可讓學習者透過由淺入深的循序漸進方式學習取得微軟新一代 MCSA: Machine Learning 認證,為最有效率學習技術實力與完成認證考試的認證方案。




本課程為微軟原裝課程,並附原裝 LAB 即時實習,由微軟認可導師 (Microsoft Certified Trainer) 教授。全個課程均為一人一機實習,理論與實戰並重。

課程附送 MCSA全套 Digital Microsoft 原裝教材 (價值 $3,000)
 

 

課程全面教授學員有關 Machine Learning 技術。


 

免費課程首堂試讀體驗,立即報名 
 

理論: 0小時
實習: 42小時
示範: 0小時
合共: 42小時
若想更了解以上資訊,歡迎致電 3106 8211 查詢。
課程費用: $6000

課程費用無須申請任何政府基金資助。

繳費方法:

一般課程:按月等額收取列明的課程費用。本中心將不早於課程開始的一個月前收取第一期費用。除第一期的費用外,每期的費用會在課程進行期間每月的首個上學日或之後收取。

組合課程:若該課程為組合課程,本中心將按該組合課程分科收費,直至所收學費為該組合課程的等額費用為止。

退款安排:
本中心備有完善之退款政策及程序。學生將會於報讀課程前獲發有關之文件,學員亦可按此閱讀。
質素保證:
本中心備有完善之免費補堂,免費重讀及彈性上課安排 (攝影課程除外),令學員更有保障。
自訂課程:
本中心歡迎各公司、機構或團體包團報讀課程,安排公司活動或同事培訓。想進一步查詢詳情,可致電熱線 3748 9826。
卓越成就:

本中心榮獲各大國際機構 (Adobe, Autodesk, Microsoft, Unity, H3C, Lenovo, Corel, Prometric, VUE, Certiport, Wacom 等等) 邀請成為香港區指定的認可教育中心及連續10 年榮獲香港社會服務聯會嘉許為「商界展關懷」公司,以表揚 Feva Works 對社會的貢獻。

除此之外,Feva Works 更連續 10 年獲 Microsoft 頒發全港最佳 Microsoft 授權培訓中心 (Best Microsoft Certified Partner for Learning Solutions of the Year) 及被 Adobe 選定為 Adobe CS4 & CS5 & CS6 & Creative Cloud 指定認可培訓中心。最近,Feva Works 更連續 10 年獲e-zone 電腦雜誌頒發最佳IT培訓中心。

注意事項:

課程內容:

MOC 20773 - Analyzing Big Data with Microsoft R

Module 1: Microsoft R Server and R Client

Explain how Microsoft R Server and Microsoft R Client work.

Lessons

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

Lab : Exploring Microsoft R Server and Microsoft R Client

  • Using R client in VSTR and RStudio
  • Exploring ScaleR functions
  • Connecting to a remote server
     

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.

Lessons

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

Lab : Exploring Big Data

  • Reading a local CSV file into an XDF file
  • Transforming data on input
  • Reading data from SQL Server into an XDF file
  • Generating summaries over the XDF data
     

Module 3: Visualizing Big Data

Explain how to visualize data by using graphs and plots.

Lessons

  • Visualizing In-memory data
  • Visualizing big data
     

Lab : Visualizing data

  • Using ggplot to create a faceted plot with overlays
  • Using rxlinePlot and rxHistogram
     

Module 4: Processing Big Data

Explain how to transform and clean big data sets.

Lessons

  • Transforming Big Data
  • Managing datasets
     

Lab : Processing big data

  • Transforming big data
  • Sorting and merging big data
  • Connecting to a remote server
     

Module 5: Parallelizing Analysis Operations

Explain how to implement options for splitting analysis jobs into parallel tasks.

Lessons

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

Lab : Using rxExec and RevoPemaR to parallelize operations

  • Using rxExec to maximize resource use
  • Creating and using a PEMA class
     

Module 6: Creating and Evaluating Regression Models

Explain how to build and evaluate regression models generated from big data

Lessons

  • Clustering Big Data
  • Generating regression models and making predictions
     

Lab : Creating a linear regression model

  • Creating a cluster
  • Creating a regression model
  • Generate data for making predictions
  • Use the models to make predictions and compare the results
     

Module 7: Creating and Evaluating Partitioning Models

Explain how to create and score partitioning models generated from big data.

Lessons

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

Lab : Creating and evaluating partitioning models

  • Splitting the dataset
  • Building models
  • Running predictions and testing the results
  • Comparing results
     

Module 8: Processing Big Data in SQL Server and Hadoop

Explain how to transform and clean big data sets.

Lessons

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

Lab : Processing big data in SQL Server and Hadoop

  • Creating a model and predicting outcomes in SQL Server
  • Performing an analysis and plotting the results using Hadoop Map/Reduce
  • Integrating a sparklyr script into a ScaleR workflow
     
MOC 20774 - Perform Cloud Data Science with Azure Machine Learning

Module 1: 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
     

Module 2: 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 : Managing Datasets

  • Prepare Azure SQL database
  • Import data
  • Visualize data
  • Summarize data
     

Module 3: 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
     

Module 4: 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

  • Prepare datasets
  • Use Join to Merge data
     

Module 5: 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
  • Create and run a neural-network based application
     

Module 6: 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
  • 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 7: 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 Models
     

Lab : Initializing and optimizing machine learning models

  • Using hyper-parameters
     

Module 8: 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
  • Consuming Experiments
     

Lab : Using Azure machine learning models

  • Deploy machine learning models
  • Consume a published model
     

Module 9: 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 language
  • Processing images and video
  • Recommending products
     

Lab : Using Cognitive Services

  • Build a language application
  • Build a face detection application
  • Build a recommendation application
     

Module 10: 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

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

Module 11: 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
  • Prepare a sample SQL Server database and configure SQL Server and R
  • Use a remote R session
  • Execute R scripts inside T-SQL statements
     

 

課程時間表:

 
MCSAMACHINEL18100015 
日期 2018/10/19 - 2019/01/18
時間 19:00-22:00 (FRI)
合共 42小時
地點 長沙灣分校
費用 $ 6000


 
MCSAMACHINEL18120015 
日期 2018/12/13 - 2019/03/21
時間 19:00-22:00 (THU)
合共 42小時
地點 長沙灣分校
費用 $ 6000