Static AI

                                            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-o...

                                            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.                                                        ...

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 m...

      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. Customizatio...

                           Machine Learning and It's Types                                 Machine Learning is ability to automatically learn and improve from experience without being explicitly programmed. So rather than typing the code for all the times and do knowledge engineering, machine learning helps the machine  to learn from previous data and find insights and pattern from it.  Basically Data is train on given data set and and applied machine learning algorithm and it find insights. Simply put, Machine learning makes a computer act and think like a human. Types of machine learning           Supervised Learning In supervised learning you use labeled data,which is a data set that has been classified, to infer a learning algorithm. The data set is u...