Development and operational tasks for better SDLC management

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Photo by air focus on Unsplash

DevOps is an application development practice that merges development tasks and operational tasks for a better software development lifecycle management, in the meanwhile also handling the frequent updates, bugs, and features of the application.

DevOps involves continuous development tasks like planning the code, coding, building the code, and testing it along with continuous operational tasks like releasing the code, deploying it, operating it, and monitoring it, followed by continuous integration of both the task sets.

DevOps core concepts:

  • The continuous build is a continuous and automated build process. It runs the added or modified codes.
  • Continuous Integration is the automated build and execution of at least unit tests to prove integration of new code with existing code but preferably integration tests (end to end). It is a practice of automatically building and unit testing an entire application frequently, ideally, on every source code check-ins, dozens of times a day, if necessary. …


Concepts of Single, Multilevel, and Multiple inheritance methods

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Photo by Markus Spiske on Unsplash

Inheritance is a method in object-oriented programming to make subclass similar to the main classes so that the subclass inherits properties from main classes. The main reason why we use inheritance is the re-usability of code.

Types of inheritance

  1. Single Inheritance
  2. Multilevel Inheritance
  3. Multiple Inheritance

Single inheritance

Single inheritance means when a subclass inherits properties from only one main class. For example, we can take the properties of the house.

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Single Level Inheritance. Photo by Author


class A:
def area(self):
print("Housing price depends on area size")

def kitchen(self):
print("The kitechen should be madular type")

#now making a class B, which inherits the properties of class A

class B(A):
def parking(self):
print("Need of space for two cars")

def garden(self):
print("Garden should be in…

Machine Learning, Statistics

Basic understanding of definitions used in data science

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Photo by Joshua Hoehne on Unsplash

Nowadays, data science aspiring buds are directly using models and algorithms without knowing so many concepts. The main idea behind this article is to get some basic concepts in data science.

Before modeling our algorithm, we need to understand how our data looks. It should not be under-fitting, over-fitting so that we can reduce the residues for a good fit.

For the machine learning model, the dataset needs to divide into the training and testing set.

We have data on time and marks, and have a relation between them is shown below.

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Data Points of the dataset


When we make a prediction with a line of a good fit, then it generates a prediction line by going through data points, as shown in the photo. For our data, the line is trying to fit best the position with minimum RMS value, i.e., root means square. The graph shown below shows that the yellow line is the line of good fit and the difference between the prediction line and data points is the residual values. We do the sum of the residual values and square them so that negative values don’t balance the positive values. To get the best fit line, we need to minimize the error, i.e., the sum of squared values should be minimum. But if we see the linear line is not the best fit line, but a good fit is a curve line. …

Natural Language Processing

A handbook for learning NLP with basics ideas

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Photo by Sincerely Media on Unsplash

Topics to be covered:

Section 1: NLP Introduction, Installation guide of Spacy and NLTK

Section 2: Basic ideas about a text, Regular expression

Section 3: Tokenization and Stemming

Section 4: Lemmatisation and Stop words

Section 5: Part of Speech (POS) and Named Entity Recognition (NER)

Let’s talk about one by one step about these.

Section 1:

Introduction about NLP

Natural Language processing comes under the umbrella of the Artificial Intelligence domain. All computers are good with numerical data to do processing, this class of section is dealing with text data to analyze different languages in this world.

In this article, we will do a morphological study in language processing with python using libraries like Spacy and NLTK. …


Handy concepts on class objects in python

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Photo by Clint Adair on Unsplash

Python is an object-oriented language and the basis of all data types are formed by classes. Its variable assignment is different from c, c++, and java. The variable does not have a declaration, it is just an assignment statement.


Python Objects

Python is a dynamically typed language. It has no knowledge about the variable’s datatype until the code executes. Hence, a declaration is of no use. The value is stored at some memory location, which is bound up with the identifier and makes the contents of the container accessible through that identifier. …


Amit Chauhan

Data Science Enthusiastic | Data Visualization | BI | NLP |

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