Static AI

      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...

                                                                                                                  History of DataScience Data Science and machine learning are now trending technology in today's industries either on the internet, education , healthcare they are making an impact. The company is using its data to gain insights from it (know their customer's behavior, product popularity and lots of various factors). Today Data is the next oil for industries. If you have a doubt about the future of data science then you are surely concerned regarding the techniques and tools like Python, Hadoop or SAS. Whether they would become outdated or whether investing to learn data science will b...

                                 Beginners guide for Machine Learning Machine learning is nowadays a highly trending domain in computer science and many students, researchers, professors and experts using this technology to solve real-world problems human interaction. Machine learning is a field of computer science in which the computer learns the finding pattern and insights from the data that are feed into the learning algorithms. So machine learning makes a big impact in the field of science by solving high complex and data-based tasks. So the rapid growth of this technology makes the interest of a huge number of people from a different domain to learn this technology and solve real-world problems and help the communities using machine learning. In this article, we are going to discuss how a beginner should start in the field of machine learning. Machine learning is a combination of e...

Top 5 ways to Clean the Data and Make it useful for Analysis Data Science is a highly trending and most popular technology of the 21st century. To become a skillful data scientist or a data analyst proficiency in statistics, mathematics, and knowledge of data analytics tools required.  Data is everywhere internet, multimedia, images and many more but to manage the data and gains some insightful information from it a quite tedious task and requires proper analysis and experience in this field. T he most challenging task is to clean the data and make it in a structured format.  There are various ways to clean data and become a proficient Data Analyst. Because 80% of the time is utilized in cleaning the data. So its a challenging and important task. Sources of missing Values in data Programming Error. The user forgot to fill it. Data was lost while transferring manually from the legacy database. User choices not to fill it ...