Data Science - 103 (Kapil Sharma)

Cloud Computing Basic:- 

In this the PC is on service provider data center and secuirty maintenance and upgrades are done by the service provider. Compute power is elastic. (Upgrade or downgrade) 

Different services, different modules as per need and change basis, service/modules can be engaged and dis-engaged as per client requirement basis. The cloud computing pricing concept based on user, pay-as-per-you-go or cloud resource consumption basis. 

Cloud computing can be scale / increase on two basis. 

One is Vertical Scaling, where user increase compute CPU, increase RAM or HDD/SSD on need basis. Second, one is Horizontal Scaling, where user increase more parallel servers as demand increases. 

Major Cloud Computing Types:- 

1. IaaS - Infrastructure as a service

2. PaaS - Platform as a service

3. SaaS - Software as a service

Data Science - 102 (Kapil Sharma)

Basic AI and ML Definitions:-

AI:- Artificial Intelligence is a software which mimics human behavior and capabilities. 

It consists of these follwing features:-  Computer Vision, Machine Learning, Natural language processing.

• Machine learning - This is often the top level for an AI system, and in this, human "teach" a computer model to make prediction and draw conclusions from input dataset.
• Computer vision - The capability of software to interpret the world visually through cameras, video, and images. use in driving, militry and many more applications.
• Natural language processing - The capability for a computer to interpret written or spoken language, and respond in kind. For example live lanaguage translation either audio syntax or image.  

 

Data Science - Class 101

Data Science:- "Obtain > Scrub > Explore > Model > Interpret"

1.) Obtain data in form of structured, un-structured, semi-structured etc. In any type, SQL, NoSQL, .CSV, Excel, JSON etc. 

2.) Clean the data either ETL or ELT (Extract Transform Load or Extract than Load and than Transform)

3.) View the data, check its "value, variety, veracity, volume, velocity".

4.) Model the data as per requirement, so that it can be process as per data model.

5.) Interpret as per analytics or as per analysis of that modeled data.