Beginning of the Future

Photo by Lucas Benjamin on Unsplash Data is a collection of facts and figures, observations, or descriptions of things in an unorganized or organized form. Data can exist as images, words, numbers, characters, videos, audios, and etcetera. What is data preprocessing To analyze our data and extract the insights out of it, it is necessary to process the data before we start building up our machine learning model i.e. we need to convert our data in the form which our model can understand. Since the machines cannot understand data in the form of images, audios, etc. Data is processed in the form (an efficient format) that it can be easily interpreted by the algorithm and produce the required output accurately. The data we use in the real world is not perfect and it is incomplete, inconsistent (with outliers and noisy values), and in an unstructured form. Preprocessing the raw data helps to organize, scaling, clean (remove outliers), standardize i.e. simplifying it to feed the data to the m

(Photo by  Chris Liverani  on  Unsplash ) Numpy  ( stands for — Numerical Python ) is a library available in Python programming language, supporting matrix data structures and multidimensional array objects. This the most basic scientific computing library that we need to learn, to begin our journey in the field of data science. Numpy can compute  basic mathematical calculations  to make the process of creating advanced machine learning and artificial intelligence applications easier (by using comprehensive mathematical functions available within the library). Numpy allows us to  carry out various complex mathematical calculations effortlessly  along with several top-up libraries (like matplotlib, pandas, scikit-learn, etc.) built over it. This library is a great tool for every data science professional to  handle and analyze the data efficiently . Moreover, it is much easier to perform mathematical operations with numpy arrays in comparison to python’s list. Numpy library has various