ūüďöAbout This lecture focuses on the theoretical as well as practical aspects of the Support Vector Machines. It is a supervised learning model associated with learning algorithms that analyze data used for classification and regression analysis. Developed at AT&T Bell Laboratories by Vapnik with colleagues (Boser et al., 1992, Guyon […]

This tutorial is brought to you by DataCamp. The tutorial does the most in rigorously explaining the little bits and pieces of the wonderful Matplotlib. Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things easier. Contents of […]

SciPy is a free and open-source Python library used for technical computing and scientific computing. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering. In this lecture, we introduce all SciPy’s functions one will most probably pass through when Jupyter Notebook on Google Chrome, which is one of […]

This lecture provides everything you need to know on Pandas Programming. I use one of my favorite environments, that is Google’s Jupyter notebook. It’s very easy to use and I’m sure you will love it. This lecture deals with Pandas DataFrames, Multidimensional Hierarchical Indexing, Boolean Operations on DataFrames, Aggregate Functions, […]

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Numpy¬†is an array-processing package. It is the fundamental package for scientific computing with Python. If you’re working with matrices, you’re going to love working with NumPy. In this lecture, we talk about the most important functionalities that NumPy has to offer. In a nutshell and from an NumPy perspective, we […]