Merge pull request #90 from microsoft/AnkitaSinghIE-patch-1

update readme

5-Data-Science-In-Cloud/18-Low-Code/README.md

@@ -30,21 +30,27 @@ Table of contents:
 
 [Pre-lecture quiz]()
 
-1. What is the cloud?
+1. What is the cloud computing?
+   1. Delivery of computing services over the internet.
+   2. Setting up your own datacenter.
+   3. Using the internet.
+2. What is the cloud?
    1. A collection of Databases for storing big data.
    2. TRUE : A collection of pay-as-you-go computing services over the internet.
    3. A visible mass of particles suspended in the air.
-2. What are some advantages of the cloud?
+3. What are some advantages of the cloud?
    1. TRUE : Flexibility, Scalability, Reliability, Security
    2. Flexibility, Scalability, Reliability, Variability
    3. Clarity, Scalability, Reliability, Variability
-3. Which one is not necessarily a good reason for choosing the cloud?
+4. Which one is not necessarily a good reason for choosing the cloud?
    1. Using Machine Learning and data intelligence services
    2. Processing large amounts of data
    3. TRUE : Storing sensitive/confidential governmental data
 
 ## 1. Introduction
 ### 1.1 What is Azure Machine Learning?
+
+The Azure cloud platform is more than 200 products and cloud services designed to help you bring new solutions to life.
 Data scientists expend a lot of effort exploring and pre-processing data, and trying various types of model-training algorithms to produce accurate models. These tasks are time consuming, and often make inefficient use of expensive compute hardware.
 
 [Azure ML](https://docs.microsoft.com/azure/machine-learning/overview-what-is-azure-machine-learning?WT.mc_id=academic-40229-cxa&ocid=AID3041109) is a cloud-based platform for building and operating machine learning solutions in Azure. It includes a wide range of features and capabilities that help data scientists prepare data, train models, publish predictive services, and monitor their usage. Most importantly, it helps them to increase their efficiency by automating many of the time-consuming tasks associated with training models; and it enables them to use cloud-based compute resources that scale effectively, to handle large volumes of data while incurring costs only when actually used.
@@ -79,6 +85,7 @@ Each way has its own pros and cons. The Low code/No code way is easier to start
 
 Cardiovascular diseases (CVDs) are the number 1 cause of death globally, accounting for 31% of all deaths worldwide. Environmental and behavioural risk factors such as use of tobacco, unhealthy diet and obesity, physical inactivity and harmful use of alcohol could be used as features for estimation models. Being able to estimate the probability of the development of a CVD could be of great use to prevent attacks in high risk people.
 
+
 Kaggle has made a [Heart Failure dataset](https://www.kaggle.com/andrewmvd/heart-failure-clinical-data) publically available, that we are going to use for this project. You can download the dataset now. This is a tabular dataset with 13 columns (12 features and 1 target variable) and 299 rows. 
 
 |    | Variable name             | Type            | Description                                               | Example           |