Top 5 ML Libraries every Data Scientist must know in 2021

Unless you are in the research field of ML, you are bound to use an ML library to complete your day-to-day work as a Data Scientist or a Machine Learning Engineer. Your ability to complete a project depends on the ML library you know; the client may want to you complete an NLP task in NLTK even though you know TensorFlow. So, here are a number of ML libraries that you must know as a Data Scientist or a Machine Learning Engineer to become quite versatile in your line of work.

1. Numpy

Written in 2006 by Travis Oliphant, this is one of the libraries that you cannot work without. Short for NUMerical PYthon, this library can do wonders when it comes to working with matrices.

Numpy uses unique array-like structures called ndarrays that can perform complex matrix-based computations within a very short period of time (speak milliseconds). Although Numpy has been made popular in Python, this was written in C, which makes it so fast.

Features:

a. Multi-dimensional array computations
b. Complex mathematical functions
c. Used for linear algebra, Fourier Transformation, etc.
d. Excellent community
e. Utilized by TensorFlow for backend calculations with Tensors
f. Can be integrated with C, C++, and FORTRAN

2. Pandas

This too was written in C, and have been there since 2008. Originally written by Wes McKinney, the Pandas library is now maintained by its own community of users. After Numpy, Pandas is the second popular library used by Data Scientists and Machine Learning Engineers.

Pandas is used to manipulate tabular data, the kind of data that you can see in a database. This can be any dataset, from Iris, to the more complete Fashion MNIST, all of these can be represented in a table form, and thus can be manipulated by Pandas.

You can use Pandas to query this data, dice-and-slice the data, visualize the data, and a lot of other useful functions. It uses 2 main data structures called a Dataframe and a Series.

Features:

a. Used to manipulating tabular data
b. Visualize data
c. Query data
d. Can be integrated with SQL
e. Used for handling uneven Time Series data

3. Scikit Learn

Right after Numpy and Pandas, comes the most useful ML library called Scikit Learn. Created around 2007 by David Cournapeau and Inria, this ML library is used mainly for data processing and data modelling.

This is the library of choice for most ML practitioners when it comes to machine learning algorithms. From performing simple linear regressions to complex NLP projects, Scikit Learn can do it all with ease.

Written using C and Python, Scikit Learn is built to complement both Numpy and Pandas. However, the only drawback for Scikit Learn is its inability to support large scale distributed computing. Other than this small glitch, Scikit Learn is a life saver for many of us.

Features:

a. Used for ML algorithms, data mining, and analysis
b. Has provision for both supervised and unsupervised learning algorithms
c. Good community support
d. Can perform classification, regression, clustering, dimensionality reduction, NLP, and other complex tasks

4. TensorFlow

Developed by the Google Brain team in 2015, TensorFlow is one of the most popular Deep Learning libraries available. This library provides an out-of-the-box approach when it comes to developing Deep Learning models for production.

TensorFlow also provides a simple, yet powerful, visualization tool for it’s Deep Learning models called the TensorBoard. Using the TensorBoard, one can visualize the performance, parameters, gradients etc. for their Deep Learning model.

It comes in many flavors like TensorFlow Lite (for embedded systems) and TensorFlow Serving.

Features:

a. Developed by Google
b. Provides C++ and Python APIs
c. You can find all the help you need in their documentation alone
d. Great community of users
e. Visualization of models using TensorBoard

5. NLTK

Short for Natural Language ToolKit, this library gives you all the help that you need when it comes to your NLP project. From language modelling to named entity recognition, neural machine translation, NLTK supports them all.

Developed in 2001 by Steven Bird, Edward Loper, and Ewan Klein, this is one of the basic libraries that one can use for NLP.

Features:

a. Excellent documentation
b. Offers support for n-grams
c. Named entity recognition

It does not matter which ML library you use, these 5 libraries are a must know since they gives you a good, strong fundamental concept of what is going on underneath the hood of your ML model. Once you have that, working on any other library is a breeze.