Important Python libraries for Machine Learning in 2022

We do not have to write ML algorithms in Python from scratch. Instead, we use libraries in Python, with the help of the library we can create ML models as well deploy it too.

What are Libraries in Python?

In simple words, a library is a kind of package in which some pre-defined code, classes, methods, and functions are kept and whenever the user (coder) has to use these functions or methods. So the coder first calls the library and then uses those functions and methods from that library in his code. A library is also called a package.?

To use libraries in Python you just have to type import <library name> like: import pandas.

Pandas

Pandas is a Python library that works smoothly with "relational" or "labeled" data. It provides a fast, flexible, and expressive data structure. Pandas aims to analyze practical and real-world data in Python.

Whenever you have to load the data for the machine learning model, you have to take the help of Pandas. Because the Panda library loads the data in DataFrame means in rows and columns, due to which the data is easy to read and understand.

NumPy

NumPy is a fast as well foundational package for scientific computing in Python, maybe you heard the term array but with the help of NumPy, you can make multidimensional arrays and these kinds of arrays are mostly useful in the Machine learning process. It has fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selection, and I/O.?

Matplotlib

Matplotlib is a library for creating 2D plots of arrays in Python. It is independent and can be used in a Pythonic, Object Oriented manner. Matplotlib is primarily written in pure Python.

Matplotlib is designed with the perspective that you can create simple plots with just a few commands or just one command. In simple words, it is a library for creating graphs and charts on top of data and helps you to get insights from data in a Visualized way.

Seaborn?

Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for creating attractive and informative statistical graphics, and I must say if you use seaborn before using Matplotlib, I don't think you will get back to Matplotlib because seaborn has such an interactive interface of graphs and charts.

Requests

Requests is a Python library that allows you to easily send HTTP requests. There's no need to manually add a query string to your URL or form -encode your POST data.

The HTTP request returns a response object with all the response data such as (content, encoding, status, etc.). This library is for getting data from URLs.

BeautifulSoup

BeautifulSoup is a Python library for extracting data from HTML and XML files. The BeautifulSoup library is mostly used for web scraping. Whenever you have to collect data from the website. So you can use BeautifulSoup to parse that data.

It works with your preferred parser to provide ways to navigate, find and modify the parse tree. This usually saves the programmer hours or days of work.

Scikit Learn

Scikit-learn is an open-source machine learning library that supports supervised and unsupervised learning.

It also provides tools for Model Fitting, Data Preprocessing, Model Selection, Evaluation, and many other utilities.

To create an ML model you just have to import this library into your Python program and then you can write any algorithm of Machine Learning with the help of this library, Scikit -learn provides dozens of built-in machine learning algorithms and models, called Estimators.

TensorFlow?

TensorFlow is a machine learning library that makes it easy for beginners and experts to build machine learning models for desktop, mobile, web, and cloud.

This package provides a library of workflows for developing and training models using Python or JavaScript and is used to easily deploy them to the cloud, browser, or device, no matter what language you use.

You can use Scikit -learn or TensorFlow as per your choice but in my perspective as a beginner in machine learning you should go with Scikit learn first because it will help you to understand the core concepts and workflow of ML algorithms, then you can move towards the TensorFlow library.

NOTE: I'm not an expert, it's just my opinion??.

Keras

Keras is a deep learning API written in Python, which runs on top of the machine learning platform TensorFlow, it was developed with a focus on enabling rapid use.

Being able to go from idea to result as quickly as possible is the key to doing good research, Keras allows you to focus on those parts of the problem and takes the load off the developer. Simple workflows should be quick and easy.

Keras adopts the principle of progressive disclosure of complexity and it offers industry-strength performance and scalability. It is used by organizations and companies including NASA, YouTube, and Waymo.

Pillow

Pillow is a friendly petition by Alex Clark and contributors. The PIL (Pillow) Python Imaging Library adds image processing capabilities to your Python interpreter, it supports extensive files.

Pillow provides an efficient internal representation and very powerful image processing capabilities. The Core Image library is designed to allow faster access to data stored in some basic pixel formats.

It provides a solid foundation for common image processing tools. Python pillow package can be used for creating thumbnails, converting from one format to another and printing images, etc.

OpenCV

OpenCV (Open Source Computer Vision Library) is a library of programming functions primarily aimed at real-time computer vision.

Originally developed by Intel, it was later supported by Willow Garage, then Etsy (which was later acquired by Intel). It is cross-platform, open source, and comes under the Apache 2 license.

If you are interested in Computer Vision, then this Python Library will prove to be very helpful for you. Using this library you can create Object Detection, Face Detection, Augmented Reality, and many more programs.

Django

Django is not a library but an open-source Python web framework that is used for fast development, practical, maintainable, clean design, and secure websites.

The main goal of the Django framework is to allow developers to focus on the components of the application that are new instead of spending time on components that are already developed.

After learning Python, learning Django can be considered a good option and it can also be useful to you in machine learning because after learning Django, you will know about website development and if you want to embed your machine learning model on the website. So you can do that too and Django is quite a useful framework.

Flask?

Flask is also a web framework like Django but it is a micro web framework that is written in Python. It’s a microframework because it comes with a very basic setup.

It means that you have to choose which template engine to use, ORM, caching, authentication, routing, request handling, sessions, and so on.

It will make your web application lighter because you probably don’t need all those features. Flask will also help you to create an API for the ML models.

NLTK

The Natural Language Toolkit, or NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English written in the Python programming language.

It provides an easy-to-use interface for over 50 corpora and lexical resources such as WordNet, as well as a set of text processing libraries for classification, tokenization, stemming, tagging, parsing, and so on.

NLTK is used to work on human language data and it is suitable for engineers, students, teachers, researchers, and industry users alike. It is available for Windows, Mac OS X, and Linux. The best part is that NLTK is a open-source and a community-driven project.

Conclusion

There are so many libraries in python such as NuPIC, PycURL, Tornado, Ramp, Pipenv, Bob, PyTorch, PyBrain, MILK, Dash, Scipy, Theano, SymPy, Caffe2, Hebel, FastAPI, Chainer, Bokeh, and a lot to discover that's the reason we call Python has a vast community.

But as a beginner in the field of the data Industry whatever the 10-13 libraries I shared in this article are sufficient enough to start your journey, and even you don't use all these at a one pace, like you start with Pandas, NumPy, and Matplotlib/Seaborn than for ML part you can go with Scikit-Learn/TensorFlow and for DL and natural language processing you can use Keras/PyTorch or NLTK as per your familiarity.

For computer vision, you will use OpenCV, and then the deployment part comes so, for that you can go with Flask or Django and there are also so many python libraries to create an API for your model. So, learn, do practical's and Explore things.