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List of Important Libraries for Machine Learning and Data Science in Python
Latest   Machine Learning

List of Important Libraries for Machine Learning and Data Science in Python

Last Updated on July 25, 2023 by Editorial Team

Author(s): Suhas Maddali

Originally published on Towards AI.

Understanding the use of various libraries in python and machine learning is handy for professionals in the field of data science. It makes life easier for data scientists or machine learning engineers.

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Candidates who pursue masters in data science and machine learning are in high demand, especially by industries in automobile or retail industries. Furthermore, having experience in the field can add a lot of credibility and trust in your competency for these roles. There are a large number of data science courses that are available online that teach the fundamentals of this field, and they are making candidates quite job-ready to be using machine learning in their day-to-day lives.

When we talk about machine learning, we always consider the possibility of using languages like Python. There are other languages, such as Java or C but they have limited potential for machine learning applications. Python is being used in a large number of applications and is currently replacing a list of other programming languages. Therefore, the best bet would be to use various libraries in python so that they can be handy for our machine learning use cases. Rather than typing the code manually and doing things from scratch, the use of libraries can make the development of machine learning code quicker and easier for professionals in the field.

Hence, let us now go over a list of all the libraries in python that are useful for machine learning and data visualization, respectively. Below are the libraries that are quite used for the most part in the field of machine learning.

List of Libraries

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Pandas: It is used in machine learning and data science for reading and manipulating the dataframes that are most popularly used in Python. It can perform a vast number of tasks starting from reading the files in the form of ‘.csv’ and ‘.xlsx’ and also performing data visualization, respectively. It is the initial library that is often used at the start of a machine learning project.

Matplotlib: It can be used for visualizing our dataset and inspecting whether it contains any null or missing values or whether there are extreme values (outliers) as well. Furthermore, it can also be used to find the importance of various features in determining the outcome (target variable). It is used after reading the data and performing exploratory data analysis (visualization).

Seaborn: It is another library that has a similar use case as that of matplotlib. There are subtle differences, however, when we are using them together. Seaborn is mainly used for a complex set of visualizations from data instead of just using 2D plots, which are given by matplotlib. It is also a good thing to consider and note that seaborn is built on top of matplotlib for visualization. Therefore, the main purpose of seaborn is to help programmers visualize the data in a large number of ways.

NumPy: This is a library that is used for performing computations efficiently in the form of arrays. In machine learning, we often deal with datasets that are large in size, and performing inefficient computations can waste a lot of time, especially when performing hyperparameter tuning before reaching the best model for deployment. In such cases, considering ways to perform computations effectively can be useful for the development cycle of a machine learning project. Using NumPy can help in performing computations quite effectively and is a friendly tool used by most data scientists and machine learning engineers.

TensorFlow: There are plenty of applications of deep learning, especially in tasks such as computer vision and natural language processing. If considering using the above, your best bet would be to be using TensorFlow, as it provides a large number of tools and technologies which are used for deep learning. There is also GPU support for TensorFlow allowing professionals to parallelize their workflow with ease with the least amount of effort and time for training and model predictions. Hence, this library can be handy for deep learning applications.

Keras: If you are developing an application with the help of deep learning, the chances are that you might not need additional customization tools such as tuning the learning rate or many others. In such cases, Keras can be a good alternative to TensorFlow. In the latter, one would have to write a large number of definitions and also place additional constraints before running deep learning models. On the other hand, Keras, which is built on top of TensorFlow, can be easier to deploy and also run with limited code without a lot of customization capabilities.

Natural Language Processing Toolkit (NLTK): If your goal is to build chatbots that can answer questions with the help of natural language processing, it would be great to use this library. It contains a list of attributes that are used to preprocess the sentence and remove various words that do not add a lot of meaning to the text. There are other things that can be done with the NLTK library, such as converting a given string into a form of a vector so that it can be understood by the machine learning models for predictions.

Scikit-Learn: Of course, the most useful and important library in machine learning is the one just mentioned. It contains a list of all the machine learning models which could be imported and used for our predictions on the training and the test data. Furthermore, there can be additional tasks that can also be performed, such as feature engineering of the datasets before they are given to models for predictions. There is a large amount of documentation available on the internet about this library, and many examples are present for easier understanding.


After taking a look at the list of libraries, I hope it makes life easier for developers to use the information and perform the tasks as efficiently as possible. Having a solid grip on these libraries allows data scientists to iterate quickly and develop more robust models in a short span of time, impressing the business and the stakeholders. Thanks for taking the time to read this article.

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