Python Libraries Tutorial By Perfect E Learning

This Video will guide you in making the best decision for your project, so let's get started with the major Python libraries and packages that you might employ in your project. Starting with machine learning libraries, we'll move to general-purpose libraries...

What is machine learning?

Machine learning (ML) is the study of computer algorithms that learn and improve automatically through the experience without being explicitly programmed. In other words, machine learning focuses on applications that learn from experience and improve their decision-making or predictive accuracy over time.

As per the Survey, NumPy, Pandas, and Matplotlib are the most chosen python libraries for machine learning.

What Is Numpy?
NumPy, or Numerical Python, is a library that contains multidimensional array objects as well as a collection of routines for processing those arrays. Besides that, it’s the fundamental package for scientific computing with Python. It contains various features - N-dimensional array objects, sophisticated (broadcasting) functions, tools for integrating C/C++, and Fortran code are few.

TensorFlow and other libraries use Numpy internally for performing various operations on Tensors. Array interface is the best and the most important feature of Numpy.

What is Pandas?
Pandas is a machine learning library in Python that implements high-level data structures and a broad variety of tools for analysis. One of the vast features of this library is the skill to translate complex operations with data utilizing one or two commands. Pandas have many inbuilt methods for grouping, combining data, and filtering, as well as time-series functionality.

What is Matplotlib?
Matplotlib provides a lookalike of MATLAB interface and an attractive user experience. Its learning curve is very smooth, with clear functions. It offers standard GUI toolkits like Qt, wxPython, GTK+, or Tkinter to give programmers an object-oriented API to embed graphs and plots into their applications.

Pillow & MoviePy
Pillow is a branch of the PIL library, a collection of functions that support Python to communicate with images. The same idea applies to MoviePy while operating with videos.

Pillow gives you the ability to work with a broad variety of formats, from the several common ones like PNG, JPEG, and GIF, to the more difficult ones, like PSD. The library further provides functions to change images through filters that can detect edges, smooth, blur, or sharpen the image; these are the methods that can enhance the visual quality.

Scikit-Learn
There are a lot of changes being made in the Scikit-Learn library. One change is the cross-validation feature, giving the ability to use more than one metric. Lots of training methods like logistics regression and nearest bystanders have gained some little improvements. There are a huge number of algorithms – beginning from clustering, factor analysis, principal component analysis to unsupervised neural networks.

OpenCV
When the tasks get more difficult, you have to use the OpenCV library. This Python library goes deep into image analysis and modification, allowing the user to solve most computer vision tasks with ease. It also operates with video files - it reads videos frame by frame and interprets them as a single or group of images. You can find and track objects in a view or even divide the view between background and foreground.

Keras
Keras is very fit for data scientists studying to create deep learning models like neural networks. Developed on the head of Theano and TensorFlow, Keras simply helps to develop a neural network. Though, this library is relatively slow to the other libraries since it tends to create a computational graph using back-end infrastructure. It gives the best services for compiling models, processing data, and visualizing graphs. The library is very famous amongst CERN and NASA organizations.

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