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Machine Learning In The Cloud With Azure Machine Learning

Video Description

Introduction to Machine Learning in the Cloud with Azure Machine Learning

About This Video

  • Azure Machine Learning is a cloud-based data science and machine learning service which is easy to use and is robust and scalable like other Azure cloud services.
  • It provides visual and collaborative tools to create a predictive model which will be ready-to-consume on web services without worrying about the hardware or the VMs which perform the calculations.
  • The advantage of Azure ML is that it provides a UI-based interface and pre-defined algorithms that can be used to create a training model. And it also supports various programming and scripting languages like R and Python.

In Detail

With the arrival of cloud computing and multi-core machines, we have enough compute capacity at our disposal to churn large volumes of data and dig out the hidden patterns contained in these mountains of data. This technology comes in handy, especially when handling Big Data. Today, companies collect and accumulate data at massive, unmanageable rates for website clicks, credit card transactions, GPS trails, social media interactions, and so on. And it is becoming a challenge to process all the valuable information and use it in a meaningful way. This is where machine learning algorithms come into the picture. These algorithms use all the collected “past” data to learn patterns and predict results or insights that help us make better decisions backed by actual analysis. You may have experienced various examples of machine learning in your daily life. Machine learning is used to build models from historical data, to forecast the future events with an acceptable level of reliability. This concept is known as predictive analytics. To get more accuracy in the analysis, we can also combine machine learning with other techniques such as data mining or statistical modeling. This progress in the field of machine learning is great news for the tech industry and humanity in general. But the downside is that there aren’t enough data scientists or machine learning engineers who understand these complex topics. Well, what if there was an easy to use a web service in the cloud, which could do most of the heavy lifting for us? What if it scaled dynamically based on our data volume and velocity? The answer is the new cloud service from Microsoft called Azure Machine Learning.

Table of Contents

  1. Chapter 1 : Welcome and Introduction
    1. Welcome and introduction 00:04:29
    2. Course overview 00:01:58
    3. Prepare for the course 00:02:10
    4. Why learn Azure ML? 00:03:07
    5. Downloadable course material 00:00:36
    6. About us 00:03:35
    7. About you 00:01:54
    8. About you 00:01:50
  2. Chapter 2 : Get Familiar with Azure Machine Learning
    1. Introduction to Azure Machine Learning 00:00:53
    2. Introduction to supervised machine learning 00:06:46
    3. Introduction to Azure Machine Learning 00:08:12
    4. Azure Machine Learning Algorithms 00:05:30
  3. Chapter 3 : Deep Dive into Azure Machine Learning
    1. Azure ML - deep dive 00:00:48
    2. Introduction to Azure ML Studio 00:06:55
    3. Deep dive into Azure ML Studio 00:04:32
    4. Doctors' appointments dataset 00:05:14
    5. Explore doctors' appointments dataset 00:06:20
    6. Prepare the dataset 00:11:14
    7. Build the Azure ML experiment 00:06:29
    8. Run the Azure ML experiment 00:01:51
    9. Visualize the results 00:05:26
    10. Deploy the web service 00:08:35
  4. Chapter 4 : Predicting Housing Prices with Azure Machine Learning
    1. Quick checkin 00:02:13
    2. Predicting housing prices with Azure ML 00:00:38
    3. Review housing pricing dataset 00:07:20
    4. Visualize housing pricing dataset 00:07:33
    5. Edit column metadata 00:08:51
    6. Edit column metadata 00:07:46
    7. Edit column metadata 00:03:22
    8. Run the experiment 00:08:31
  5. Chapter 5 : Deploy a Web Application with Azure Machine Learning
    1. Deploy a web application with Azure ML 00:00:39
    2. Introduction to Azure cloud 00:04:58
    3. Deploy Azure ML web application 00:12:00
    4. Review Azure ML web application 00:01:59
    5. Test drive Azure ML web application 00:09:51
    6. Wrap up 00:01:40
  6. Chapter 6 : Conclusion
    1. Thank you! 00:03:52