packages

# 10 Popular packages in python

1. REQUESTS

Requests is a Python module that you can use to send all kinds of HTTP requests.
It is an easy-to-use library with a lot of features ranging from passing parameters
in URLs to sending custom headers and SSL Verification.Requests allow you to send 
HTTP/1.1 requests. You can add headers, form data, multi-part files, and parameters
with simple Python dictionaries, and access the response data in the same way.
Installing The Requests module
$ pip install requests
Or
$ easy_install requests

2.TensorFlow

This library was developed by Google in collaboration with Brain Team.
TensorFlow is used in almost every Google application for machine learning.
It is a open source library.TensorFlow is optimized for speed,it makes use 
of techniques like XLA for quick linear algebra operations.It works like a
computational library for writing new algorithms that involve a large 
number of tensor operations.

3.Scikit-learn

It is a Python library is associated with NumPy and SciPy. It is considered
as one of the best libraries for working with complex data.
There are a lot of changes being made in this library. One modification
is the cross-validation feature, providing the ability to use more than 
one metric.it contains a numerous number of algorithms for implementing 
standard machine learning and data mining tasks like reducing dimensionality,
classification, regression, clustering, and model selection. 

4.Keras

Keras is considered as one of the coolest machine learning libraries in Python.
It provides an easier mechanism to express neural networks. Keras also provides
some of the best utilities for compiling models, processing data-sets, 
visualization of graphs, and much more.

5.PyTorch

PyTorch is the largest machine learning library that allow developers to perform
tensor computations wan ith acceleration of GPU, creates dynamic computational graphs
,and calculate gradients automatically. Other than this, PyTorch offers rich APIs
for solving application issues related to neural network.

6.LightGBM

Gradient Boosting is one of the best and most popular machine learning library, 
which helps developers in building new algorithms by using redefined elementary
models and namely decision trees. Therefore, there are special libraries which
are designed for fast and efficient implementation of this method.these libraries
are LightGBM, XGBoost, and CatBoost

7.Eli5

Most often the results of machine learning model predictions are not accurate, 
and Eli5 machine learning library built in Python helps in overcoming this challenge.
It is a combination of visualization and debug all the machine learning models and 
track all working steps of an algorithm.

8.SciPy

SciPy is a machine learning library for application developers and engineers.
SciPy is a library that uses NumPy for the purpose of solving mathematical functions.
this uses NumPy arrays as the basic data structure, and comes with modules for various 
commonly used tasks in scientific programming.
Tasks including linear algebra, integration (calculus), ordinary differential equation
solving and signal processing are handled easily by SciPy.

9.Theano

Theano is a computational framework machine learning library in Python for computing
multidimensional arrays. Theano works similar to TensorFlow, but it not as efficient
as TensorFlow. Because of its inability to fit into production environments.Moreover,
Theano can also be used on a distributed or parallel environments just similar to
TensorFlow.

10.XGBoost

XGBoost is an algorithm that has recently been dominating for structured or tabular data.
XGBoost is an implementation of gradient boosted decision trees designed for speed and
performance.

11.Lightning

The name "Lightning" is simply a reference to the Open Watcom logo.It is incomplete
It does not imply this implementation is any faster than stock Python. In fact, it is
consdierably slower at the moment.Lightning Python is an alternate build of Python
for Microsoft Windows and Linux using the Open Watcom compiler.

12. PYGAME

The pygame library is an open-source module for the Python programming language specifically
intended to help you make games and other multimedia applications. Built on top of the highly 
portable SDL (Simple DirectMedia Layer) development library, pygame can run across many 
platforms and operating systems.By using the pygame module, you can control the logic and
graphics of your games without worrying about the backend complexities required for working 
with video and audio.
INSTALLATION:-
pip install pygame

13.  nltk

	NLTK is a powerful Python package that provides a set of diverse natural languages algorithms.
  It is free, opensource, easy to use, large community, and well documented. NLTK consists of the
  most common algorithms such as tokenizing, part-of-speech tagging, stemming, sentiment analysis,
  topic segmentation, and named entity recognition. NLTK helps the computer to analysis, preprocess,
  and understand the written text.
  !pip install nltk

14. NLP

	NLP enables the computer to interact with humans in a natural manner. It helps the computer to
  understand the human language and derive meaning from it. NLP is applicable in several problematic
  from speech recognition, language translation, classifying documents to information extraction.
  Analyzing movie review is one of the classic examples to demonstrate a simple NLP Bag-of-words model,
  on movie reviews.