API-Application Programming Interface

In Simple terms, API is a service which takes the request from the user put it in front of the server and gets response from the server and presents it to the user in the understandable format. It just act as a mediator to interact between two applications. Example: Uber using Google Maps API to pull out the location data.

SSH

(1)SSH stands for Secure Shell.

(2)It is a Method of securely communicating with another computer.

(3)All data sent via an SSH connection is encrypted (means if someone tries to intercept the information transferred it would appear in an unreadable format).

(4)SSH is based on Unix Shell, which is a program that interprets the command entered by user.

(5)Standard Unix command can be used to view, modify and transfer files once an SSH connection is established.

How to customize Django Admin panel ?

1) Go to admin.py file
2) Create a class {modelname}Admin
3) admin.site.register({modelname}, {modelname}Admin)
4) Save and go to admin page of your application.

Session

A session can be defined as a server side storage of information that is persist throughout the user interaction with the website or web application. We can store data in the browser of client machine to get the preferences of particular user.

How to send emails using Python ?

Sending emails through programming languages like python has many use cases.
For example, you might want to send transactional emails.

Machine Learning

Machine Learning is a subset of Artificial Intelligence (AI) which provides machines the ability to learn automatically & improve from experience without being explicitly programmed.

Netflix recommendation are made by machine learning.

Need for machine Learning:
1) Increase in Data Generation
Because of excessive production of data we need a method that can be used to structure analyze and draw useful insight from data.

2) It improves decision making
By using various algorithm machine can be used to make better business decision for ex. to forecast sales it is used to predict any downfall in the stock market.

3) It uncover patterns & trends in data
Finding hidden patterns and extracting key insights from data is the most essential part of machine learning.

Supervised and Unsupervised Learning

Supervised learning is a technique in which we teach or train the machine using data which is well labelled.
Algo: Linear Regression, Logistic Regression
Unsupervised learning is the training of machine using information that is unlabeled and allowing the algorithm to act on that information without guidance.
Algo: K-means, C-means

Internet of Things

Internet of things is the concept of connecting any device to the internet and to other connected devices. The IOT is a giant network on connected thing and people - all of which collect and share data about the way they are used and about the environment around them.
"Connecting everyday things embedded with electronics software and sensors to the internet enabling them to collect and exchange data."
Eg. Smart microwaves- which automatically cook your food for the right length of time, Self driving cars whose complex sensors detect objects in their path.

Blockchain

Blockchain is a decentralized system of secured and trusted distributed databases which records and shares the transaction details across many nodes. So, that the data is not modified.
"Structures that stores transactional records, also known as block, of the public in several databases, known as the chain in a network connected through peer to peer nodes."

How to write efficient SQL Queries

In development, writing the queries is not just important. It is also important to write efficient SQL queries. Efficient queries give faster results. When the SQL queries are optimized your application's faster performance saves a lot of time.
1) Use the exact number of columns
2) No need to count everything in the table
3) Avoid using DISTINCT keyword.
4) Create a small batches of Data for Deletion and Updation
5) Use Temp Tables

Confusion Matrix

A confusion matrix is a table summarizing the frequency of predicted versus actual class in your data.

1) This is the most common and concise way to evaluate performance and compare classification models against one another.