10 Data Science and Machine Learning Libraries for Python

Hello folks, if you want to become a data scientist or machine learning engineer and looking for the best Python libraries for data science, machine learning, data analysis, and deep learning then you have come to the right place.

Earlier, I have shared the best and resources to learn , , and , and in this article, I am going to share the best libraries python developers can learn for data science and machine learning.

But if you are a beginner in the field of Data Science and Machine learning then let me congratulate you for making the right decision and learning these in-demand skills but learning these skills is not easy, there are a lot of choices to make and each choice has its own consequences.

When I started my journey of Machine learning and Data Science, I had to first make a choice about choosing the right programming language as both and were doing great.

I eventually chose Python because of a bigger community, general-purpose in nature, and some prior experience in writing Python code. But, there was one more reason which helps me to choose , the wide range of awesome libraries available in Python.

Today, I am going to introduce you to some of those awesome libraries like TensorFlow, NumPy, Pandas, SciPy, Scikit-learn, Seaborn, Keras, and Matplotlib. I know there are many more libraries but with my limited experience and exposure, I have heard of these main libraries so far.

I'll definitely add new libraries to this list as and when I come across but till then knowing these libraries will help you a lot, particularly if you are also learning Data Science, Artificial Intelligence, and Machine learning using Python.

Whether you are a beginner or already know Data Science, learning these libraries can make you more productive and enhance your profile. By the way, if you are a complete beginner, I suggest you start with a hands-on course like which will teach you both Python and Data Science from scratch.

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10 Best Python libraries for Data Science, Analysis, Visualization, and Machine learning

Without any further ado, here is a basic introduction to some of the most popular Python libraries for Data Science and Machine learning. I have tried to keep the explanation short and sweet and pointed it out to the resource to learn more just for the sake of brevity and clarity.

As I am also learning Python and Machine learning, will write in detail about each of these libraries in the future because you would need at least one post to explain them in a little bit of detail.

1. TensorFlow

This is one of the most popular machine learning library and there is a good chance that you might have already heard about it. You might know that is from Google and was invented by their Brains team and used in the RankBrain algorithm which powers millions of search questions on Google's search engine.

In general, it is a symbolic math library and is also used for machine learning applications such as neural networks. There are many applications of TensorFlow and a lot of stories you can find on the web like how a Japanese farmer used TensorFlow to filter Cucumber.

If you are interested in learning TensorFlow then I suggest you start with , it will not only teach you TensorFlow but also the basics of machine learning and How Neural Networks.

In short, one of the , but, if you need more choices, you can also take a look at my list of best TensorFlow courses for Machine learning programmers and Data Scientists.

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2. Keras

One of the main problems with creating machine learning and deep learning-based solutions is that Implementing them can be tedious to create and require you to write many lines of complex code. Keras is a library that makes it much easier for you to create these deep learning solutions. In a few lines of code, you can create a model that could require hundreds of lines of conventional code.

If you want to learn more about Keras, I suggest you check out course by Jose Portilla on Udemy. This is the highest-rated course to learn Keras.

It's a great course by Jerry Kurata, a Solutions Architect at InStep Technologies, who is also an instructor of some of the popular machine learning courses on Pluralsight like , if you are just starting with machine learning, Jerry's courses can be a great guide.

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3. Scikit-learn

This is another popular Python library for machine learning. In fact, Scikit-learn is the primary library for machine learning. It has algorithms and modules for pre-processing, cross-validation, and other such purposes.

Some of the algorithms deal with regression, decision trees, ensemble modeling, and non-supervised learning algorithms like clustering.

If you want to learn Scikit-Learn in-depth, I suggest you enroll in course on Udemy.

It is one of the most comprehensive course on Data Science and Machine Learning with Python and along with Scikit-learn also teach you other popular machine learning algorithms like , , Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more!

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4. NumPy

NumPy is another wonderful Python library for machine learning and heavy computation. facilitates easy and efficient numeric computation. It has many other libraries built on top of it like .

You should at least make sure to learn NumPy arrays, which are basic and has a lot of applications in machine learning, data science, and artificial intelligence-based programs.

You can use the previous course (Data Science and Machine Learning Bootcamp) mentioned in the list to learn NumPy but if you are from a finance background and thinking to use NumPy, you can also take a look at course on Udemy.

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5. SciPy

This is a python library for scientific and technical computing. It will provide you with all the tools you need for scientific and technical computing.

It has modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers, and other tasks.

There is a wonderful FREE course to learn SciPy with Python, . It's my favorite and more than 100K other developers have also enrolled in it. You can check this out before it converts to the paid course.

If you need more choices, I suggest you take a look at my earlier post about the .

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6. Matplotlib

If you need plotting then Matlotlib is one option. It provides a flexible plotting and visualization library, Matplotlib is powerful. However, it is cumbersome, so, you may go for Seaborn instead.

If you want to learn Matplotlib in-depth then once again is a great course to start with

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7. Pandas

This is one of the Python libraries which is built on top of NumPy). It comes in handy with data structures and exploratory analysis. Another important feature it offers is DataFrame, a 2-dimensional data structure with columns of potentially different types.

Pandas will be one of the most important libraries you will need all the time and that's why it's very important to learn Pandas well. If you want to learn Pandas in-depth then is a great resource, to begin with.

This course will teach you about DataFrame, Merging, Joining, and Concatenating, group by and multi-index, etc.

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8. Seaborn

Like Matplotlib, it's also a good library for plotting but with Seaborn, it is easier than ever to plot common data visualizations.

It is built on top of Matplotlib and offers a more pleasant, high-level wrapper. You should learn effective data visualization.

Once again, I suggest, to learn about the Seaborn library.

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9. OpenCV

This is another important library for Python developers for computer vision. If you don't know, Computer Vision is one of the most exciting fields in Machine Learning and AI.

It has applications in many industries, such as self-driving cars, robotics, augmented reality, and much more and OpenCV is the best computer vision library.

Although you can use OpenCV with many programming languages like C++, its python version is beginner-friendly and easy to use which makes it a great library to include in this list.

If you want to learn Python and OpenCV for basic image processing and perform image classification and object detection and need a course then I highly recommend you to join on Coursera.

This is a hands-on course and will teach you an Open CV with several labs and exercises.This course is also part of multiple Specializations or Professional Certificates programs and completing this course will count towards any of the following programs:

You don't need to have any prior experience on Machine Learning or Computer Vision to join this course. However, some knowledge of the Python programming language and high school math is necessary.

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10. PyTorch

This is another exciting and powerful Python library for data science and Machine learning and something which every data scientist should learn.

If you don't know PyTorch is one of the best deep learning libraries developed by Facebook which can be used in deep learning applications like face recognition and self-driving cars, and so on.You can also use Pytorch to build machine learning models like NLP and computer vision, just to name a few. You can also use PyTorch to create deep neural networks.

If you want to learn PyTorch then I highly recommend you to join course by Joseph Santarcangelo on Coursera.

This course is also part of the which is a great program for anyone who wants to become an AI engineer.

By the way, If you are planning to join multiple Coursera courses or specializations then consider taking which provides you unlimited access to their most popular courses, specialization, professional certificate, and guided projects. It cost around $399/year but it’s completely worth your money as you get unlimited certificates.

That's all about some of the best Python libraries for Data Science, Machine Learning, and Artificial Intelligence. Depending upon what exactly you are doing with machine learning and data science, you can choose these libraries to help you out.

If you are starting afresh, I suggest you or , two of the most popular and primary libraries for machine learning.

Thanks for reading this article so far. If you find these best Python libraries for machine learning, data science, and Artificial Intelligence useful then please share them with your friends and colleagues. If you have any questions or feedback then please drop a note.

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