Several programming languages, such as Java, JavaScript, R, C, and Python, are great for machine learning projects. However, Python has become the most preferred machine learning language. While machine learning is complex, Python is easy to learn and code. As a result, most programmers prefer Python as one of their tools to solve machine learning projects. Also, it is popular in ML because it provides many machine learning libraries to solve complex problems efficiently.
This is one of the most crucial reasons programmers prefer Python for their machine learning projects. Because of all the libraries and frameworks it has to offer.
So in this article, I will list and discuss the top 13 machine learning libraries you must know if you are a Python programmer.
Related: The Ultimate Python Cheat Sheet – An Essential Reference for Python Developers
NumPy
First, let’s talk about NumPy. It is one of the most popular Python machine learning libraries and also one of my favorites.
It is a general-purpose array processing package that provides a massive collection of complex math functions that are useful to process multi-dimensional matrices and arrays of any size.
Read: NumPy Tutorial for Beginners – Arrays
This Python library can handle random numbers, linear algebra, and Fourier transforms. Not only that but, it also has powerful computational abilities similar to C.
NumPy is one of the crucial building blocks of libraries such as Scikit-Learn and SciPy. Moreover, TensorFlow uses NumPy for manipulating tensors at the backend.
Numpy is quite popular among popular IT companies. Reportedly, over one hundred and thirty companies use NumPy. For example, Tokopedia, Trivago, Walmart, Avito, Sendcloud, Sendgrid, and Instacart.
Related: NumPy Array Indexing & Slicing Explained
SciPy
Next on our list is SciPy which is one of the essential machine learning libraries for scientific computing.
This library came in in 2001 to allow programmers and scientists to solve complex mathematical problems. As mentioned earlier, NumPy is one of the building blocks of SciPy.
It offers several essential modules to handle specific areas such as image optimization, Fourier transform, linear algebra, and signal processing. Moreover, you can use SciPy to perform other computational tasks like interpolation and ODEa.
Here are some of the sub-packages that SciPy offers:
- Cluster for clustering algorithms.
- FFTPACK for fast Fourier transform routines.
- Integrate for integrating differential equation.
- IO for input and output.
- Linalg for linear algebra.
- Signal for signal processing.
- Ndimage for n-dimensional image processing.
- Stats for statistical distributions and functions.
Large companies such as Sendcloud, WanderlustAI, Uptain GmbH, Tarfin, Iziwork, Adikteev, and XICA Magellan utilize SciPy.
Knowing how to work with SciPy is a must for any data scientist or machine learning engineer.
Matplotlib
Matplotlib is also one of my personal favorites. It is one of the most popular machine learning libraries to perform data visualization.
The main focus of this library is to create 2D plotting images and figures in different formats.
With Matplotlib, we can generate error and bar charts, plots, histograms, scatter plots, and more.
One of its best features is that it allows you to perform complex image plotting and data visualization with very few codes.
Without a doubt, data visualization is a crucial part of any data analytics or machine learning project. Thus, Matplotlib is a powerful tool to have in your arsenal.
Pandas
Pandas is another popular Python machine learning library for data analysis. It provides fast and flexible data structures to work with relational and labeled data.
Pandas uses two types of data structures – Series and DataFrame. While Series is 1-dimensional, DataFrame is 2-dimensional. With their flexibility to work with large unstructured datasets, Pandas come in handy dealing with social, scientific, and financial data.
Recommended: Pandas Tutorial for Beginners – The Ultimate Guide
If you want to work for major corporations like Facebook, JP Morgan, Square, and PNC as a data scientist or ML engineer, you have to know how to work with Pandas.
PyTorch
If you want to take your machine learning skills to a new level, I suggest you learn PyTorch. It utilizes the Torch library. And apart from machine learning, you can also use it for computer vision and natural language processing projects.
PyTorch is a robust framework that offers powerful tensor computing abilities via graphical processing units (GPUs).
Moreover, another essential feature of PyTorch is its deep and powerful neural networks. This neural network runs on an automatic differentiation engine.
Moreover, we can also integrate and use other Python libraries such as NumPy and SciPy with PyTorch.
Companies such as Verizon, Salesforce, JP Morgan, and Comcast use PyTorch.
My suggestion is that after you have the basics of data analysis down, you should work on learning PyTorch. You will be amazed by the models that you can create.
Keras
Now, let’s talk about Keras which, is also one of the most popular machine learning libraries. It can run on top of multiple other libraries such as TensorFlow and Theano. And when it comes to GPU & CPU utilization, Keras is highly efficient.
This library can work with various neural network building blocks. These include objectives, optimizers, layers, and activation functions. Moreover, it also offers several options to work with deep neural network cases like features for images and text images.
Keras has a vast ecosystem that includes data management, hyperparameter training, deployment solutions, and more.
Using Keras is straightforward. Moreover, it also has other features for model deployment. Keras models can run directly on browsers, and even smartphone OS such as IOS and android, and even on embedded devices.
Many scientific organizations like NASA, CERN, and NIH utilizes Keras for their machine learning projects.
Learning Keras is a surefire way to stand out from the crowd. This library is high in demand and companies actively require professional that knows how to work Keras.
Scikit-learn
Next, we have Scikit-learn. It is one of the most popular machine learning libraries used for building machine learning algorithms.
NumPy and SciPy are also the building blocks of Scikit-learn, and it consists of several supervised and unsupervised machine learning algorithms.
Apart from solving complex machine learning problems, you can also perform fundamental data analysis and mining. Working with Scikit-learn is straightforward as it provides easy-to-use powerful tools for data analysis.
Some of the crucial functions of the Scikit-learn library are:
- Regression
- Model Selection
- Classification
- Preprocessing
- Dimensionality Reduction
Companies like Spotify, J.P Morgan, Betaworks, Inria, Evernote, Booking.com, Aweber, and many more use Scikit-learn for their ML & data operations.
Also Related: KNN Algorithm Using Scikit-Learn – Classifying Iris Species (Tutorial)
If you can get the basics of Scikit-learn under your belt, you will surely stand out because this skill is very demanding.
TensorFlow
Out of all the machine learning libraries mentioned in this article, TensorFlow may be the most popular one.
Google created and developed TensorFlow for their AI projects. Now they have made this tool open source for the world to use as well.
TensorFlow involves tensors for defining and running calculations. It is a powerful tool that allows programmers to perform high-performing numerical computations.
One of the reasons why TensorFlow is critical for AI applications is because it is capable of training and running deep neural networks.
Because of such features, Scientists use TensorFlow for conduction their deep learning research.
As mentioned, TensorFlow is extremely popular and used widely throughout the world. Some of the giant companies use TensorFlow. The list includes Coca-Cola, Airbnb, Google, Intel, Twitter, Lenovo, MI, Snapchat, PayPal, and many more.
If you can learn and master TensorFlow, then your AI & machine learning skills will be on a whole different level. It is powerful, fun, and not to mention high in demand.
Theano
Mathematics and statistics are core parts of machine learning. I have seen many programmers who get demotivated just because of math. However, Theano can be the solution to that.
Theano is a popular machine learning library to works with mathematics and statistics.
Using it, we can define, optimize, and evaluate mathematical expressions. Moreover, Theano is well efficient even with multi-dimensional arrays.
One of the features of Theano is that it very efficiently optimizes the GPU and CPU. Such features make it the best of unit testing and perform self-verification to detect and solve various errors.
Orange3
Orange3 is a classic Python toolkit for machine learning.
Apart from machine learning, we can also use it for data visualization and data mining. One of the best features of Orange3 is its high-accuracy predictive models and recommendation systems.
It comes with a variety of tools that are capable of performing high-class testing for machine learning algorithms. To create predictive models, Orange3 offers several useful widgets. Moreover, Orange3 is easy to learn.
Being an easy toolkit, Orange3 is used in schools, colleges, and universities to teach machine learning.
Caffe
One of the powerful frameworks for deep learning is Caffe. Although written in C++, the core programming language of its interface is Python.
Caffe is expressive, fast, and modular. Moreover, it also offers top-quality deep learning algorithms and models for complex machine learning projects.
As the architecture of this framework is expressive and modular, it is an efficient tool for developing machine learning algorithms and applications. Switching between GPU and CPU is quite simple with Caffe.
One of the best features of Caffe is its speed. It’s fast and gives high performance.
I believe Caffe can be a great addition to your resume. One of the reasons it’s one of the best machine learning frameworks is because of its speed. As a result, it is super efficient for industry-level deployment and speed.
Pyevolve
Genetic algorithms are popular areas of neural network research.
Working with genetic algorithms is complex. Thus, Pyevolve is a tool best for handling complex genetic algorithms.
This library is written in Python only and has a very easy-to-use API. It is a complete multi-platform framework designed to create genetic algorithms in Python. But the recent developments have also supported generic programming.
The team of Pyevolve is aiming to create a framework written in pure Python for evolutionary algorithms.
Seaborn
Last but not least, we have Seaborn. It is one of the popular ones super useful for machine learning research and projects.
Seaborn is a Python library with Matplotlib as its core. Additionally, it contains data structures from Pandas. These libraries act as a building block that allows Seaborn to perform advanced data analytics and visualization.
If you want to visualize your data to gain powerful insights, this tool will get the job done.
Some of the core characteristics of Seaborn are:
- The feature to visualize multivariate and univariate data.
- Support for visualizing regression model.
- It can perform well with other machine learning libraries such as NumPy and Pandas.
Although this library may be at the end of the list, do not get me wrong. Seaborn is very demanding and handy in professional environments. Without a doubt, this tool is one of the powerful machine learning libraries out there.
Conclusion
There could be at least a hundred machine learning libraries out there. But the ones that I have discussed here are sure to give you an edge. And the reason is these are the most in-demand and also highly used.
I am assuming that you know Python since you are already reading this article.
If not, then be sure to check out this tutorial from Linkedin Learning:
My suggestion is to start with NumPy, Pandas & Matplotlib. Then move on the some of the complex machine learning libraries such as TensorFlow or PyTorch.
You may need to adjust and learn a little bit of everything depending on your goals or projects. However, I do not recommend that path. Instead, master a few libraries and work on them until you understand the ins and outs.