NZ$157.46 – NZ$262.06

Data Science with Microsoft Cloud

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Wellington, 6001

New Zealand

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Description

Lella Etaati presents "Data Science with Microsoft Cloud"

Audience

This training is designed for data scientists, data developer or data architecture, who have the data in the cloud or thinking about using Microsoft machine learning tools in cloud scale. In this training, you will get familiar with machine learning cloud possibilities.

Description

In this one day workshop , you will learn how to Azure ML Studio as the first tool for machine learning cloud that introduced in 2014. The detail on how to create a model, how to enhance machine learning algorithms, how to import data and so forth will be explained. Then, you will get familiar with Azure Data Science Virtual Machine as a comprehensive tool for machine learning, some of the tools in it like the CNTK and Azure ML workbench will be explained. Finally, Azure Data Bricks and how to use it for the aim of machine learning will be explained too. This training has many hands-on labs, and all the required scenario will be explained step by step.

At the end of this training, the audience will learn how to define a machine learning problem and how to use Azure ML Studio for cloud machine learning and Azure Data Bricks and so forth for machine learning.

The training includes but not limited to topics below:

2.1: Azure Machine Learning (ML) Studio

In this section, you will learn some basic of machine learning and how it works. Then some introduction to Azure ML Studio will be provided.

Import data: The main component for importing data from local PC, how to import data from another workspace, how to import data from HTML website, how to import data from cloud such as Azure SQL DB.

Data Cleaning: data cleaning is the main process that we should do before any machine learning process. I will explain some of the available component in Azure ML. How to clean missing values, remove duplicate data, select column, and clip value (remove outliers). You will learn how to normalize the data, how to use SQL Statement for data transformation, how to use enter data manually, how to join data from different data resource .

Feature Selection Data Sampling: the process of feature selection will be explained, how to split data, how to partition data using a sampling approach, how to create different folds for the aim of cross-validation.

Models: a bit talk about the available models in Azure mL for predictive, descriptive, prescriptive and anomaly detection. An example of a prediction of a group, a value, clustering data will be shown. For each scenario, an example will be presented; different algorithms will apply to a problem. The main concepts of k-mean, the PCA chart will be explained.

Training and Scoring models: how to choose algorithms and how to train model and test model, what is cross-validation, how to do cross-validation to apply a model to different folds of data, how to check the different values for each parameter and see the related accuracy to that.

Evaluate: how to evaluate a classification problem concepts of Accuracy, Recall, and Precision will be discussed. How to evaluate and see the result of more than three algorithms on one dataset will be shown. Using different evaluate the model, enter data manually component and add row component.

Publish to Web: the process of creating a web service from a model will be discussed, how to check it in Excel will be shown, also how to use it in Stream Analytics as a function for data will be shown.

Sharing workspace, create projects: the process of how to share the workspace with others will be discussed, how to create a project for each experiment, how to create a trained model component for reuse, and a data transformed dataset, how to export datasets to CSV, and other formats will be shown.

Azure ML model in Power BI: Create a model in Azure ML Studio and use it in Power BI

Azure ML Model in IoT: the process of how to create a model in Azure ML model studio and apply it on streamed data.

2.2: Data science Virtual Machine

The Data Science Virtual Machine (DSVM) is a customized VM image on Microsoft’s Azure cloud built specifically for doing data science. It has much popular data science and other tools pre-installed and pre-configured to jump-start building intelligent applications for advanced analytics. It is available on Windows Server and Linux. In this session, the audience will learn

  • How to set it up
  • Environment needs
  • What parameter needs to be set up
  • What software and tools can be used
  • Deep learning introduction
  • Deep learning tools such as Tensor flow and so forth.
  • CNTK

2.3: Azure Data Bricks

Azure Databricks is an Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform. Designed with the founders of Apache Spark, Databricks is integrated with Azure to provide one-click setup, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts. Azure data bricks are one of the main platforms for the aim machine learning in this part audience will get familiar with

  • What is data bricks and how to set it up
  • The environment of data bricks and explain how they work
  • How to able to write R, Python, Scala, and SQL at the same time
  • Visualization of Databricks
  • Workspace and clusters
  • How to get information from Azure Data Lake store
  • How to get data from event Hub
  • What is notebook
  • How to pass data from one cell to other
  • How to use Databricks for a machine learning scenario storage model in Azure Data lake and show the result in Power BI

About Leila Etaati

Leila is an Artificial Intelligence Microsoft MVP, Ph.D., Trainer, and BI consultant. She is world well-known speaker in Machine Learning and Analytics topics and spoke in world’s best international conferences in Data Platform topics, such as; Microsoft USA Ignite, Microsoft data Insight Summit, Microsoft NZ ignite, SQL PASS Summits, PASS Rally, SQL Nexus, SQL Saturdays and so on. She has more than 10 years’ experience in Data Mining and Analytics. She writes blog posts in RADACAD and also publishes YouTube videos in our channel. She also is an invited lecturer in universities such as the University of Auckland, and Unitec, and some other universities. She worked in many industries including banking financial, power and utility, manufacturing, tourism, and so on.

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TBC

Wellington, 6001

New Zealand

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Refund Policy

Refunds up to 7 days before event

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