Static AI

Rising of the AI in the human centric Development The rising of the artificial intelligence in later 90's have make a rapid impact in field of technology and from 21st century the blooming of a mechanism makes several impact in various industries including software, education, healthcare and many more. As the world becomes increasingly reliant on technology, the role of  artificial intelligence  (AI) in human-centric development has risen to the forefront. From healthcare to transportation to education, AI is being used to improve the lives of people around the globe. Major areas where artificial intelligence AI makes an Impact One area where AI has made significant strides is in the healthcare industry. AI-powered virtual assistants can now assist doctors in diagnosing and treating patients, freeing up valuable time for medical professionals. In addition, AI-powered wearable devices can track a person's health and alert them to any potential issues. The transportation industry

                                            Calculate Running Time of an Algorithm The running time of algorithm defines the time required to execute an algorithm on the given set of inputs(n). There are mainly three types of complexity cases defines to measure the running time of an algorithm also known as Asymptotic analysis. 1) Best Case : Best case also called ( Ω) omega  notation which measure the best case scenario of how long an algorithm can possible take to complete given operation on (n) inputs. It's also known as lower bound. 2) Average Case : It represents by ( Θ) theta  notation which measure the average time requires to complete a given operation on set of inputs. It measures between upper and lower bound running time and calculate average running time. 3) Worst Case: It defines the worst case running time of an algorithm. Also represent using ( Ο) Big-oh. It is the upper bound of an algorithm running time and measures the worst case scenario of how long an algorithm

                                            Components of Data Science Life Cycle Data Science continues to evolve as the one of the most promising and demanding career of 21st century. The insights drawn from the data is very much useful and profitable for the businesses when processed with intelligent algorithms to find pattern and insights from it. The complete Data science follows a life cycle pattern which defines the steps of each stage of data and apply them to make it processed in more informative and easier way. The components of Data Science life cycle consist of five stages. Each stage have different tasks which perform on data during complete life-cycle span of Data science.                                                                                                     Fig:- Components of Data Science Life Cycle     The 5 components of Data Science Life Cycle are:-   1. Data Capturing Capture of Data from different  sources such that we derive some result from it after

HeatMap (Matrix) Plot Visualization for the Data: When to Use? Visual representation always helps in simplification either any real world entities or the data. Visualization  provides an pictorial representation so anyone can easily understand about the data and their insights(what they are representing and in which range the value is lying.                                                                                                                                                             Source: HeatMap Now when the data science becomes one of the popular domain in Computer science. It makes a big impact both in technology domain and in industries. Every industries now a days wants to find insights about their business data that are generated daily and improve and grow their business accordingly. So the data science jobs now become very trending. To make a complete analysis of data one's should many times go through visualization phase. Because everyone is not a good statist

      The Magic of Data Visualization Using Matplotlib Matplotlib is a multiplatform data visualization library built on Numpy arrays and designed to work with broader Scipy Stack. Matplotlib was developed by John Hunter in 2003 with version 0.1. This project is supported by Space Telescopic institute for complete development and extension for better capabilities. Matplotlib library enhances the plotting and visualization technique in python. As using the matplotlib we can create various plots, histogram, maps, chart and many more plotting. Visualization of Data     Important features of Matplotlib   It play and operates well with many operating systems and graphics back-ends.   Matplotlib have strength of running cross platform graphics engine smoothly and reliable to different types of graphics system.   There are various API’s and wrappers make this library to useful to dive into Matplotlib’s syntax to adjust the final plot output. Customization and configuration of plot styles, con