10 Best Open-source Machine Learning Libraries [2021]

Machine learning libraries and frameworks make it easier to write code for machine learning without knowing the underlying mathematics behind the algorithms or building from scratch. With libraries, we can write code faster to train models.

In picking a machine learning framework to use, carefully consider these:

1. Learning curve of a machine learning library

Some machine learning libraries are easy to learn and implement, while others require more technical expertise.

2. User and organization’s adoption

It is essential to know what tool organizations use in production if you intend to apply for machine learning jobs or build your product. 

3. Project scope

Machine learning libraries focus on different goals. Find a library that is useful to your project scope. 

If your project scope focuses on image data, you should choose better-optimized frameworks for images.

10 best machine learning libraries and frameworks.

1. PyTorch

machine_learning_libraries_pytorch-logo

PyTorch is an open-source machine learning framework developed by Facebook’s AI Research lab (FAIR)

Written in: Python, CUDA, C++.

PyTorch is used both for research and production in building state-of-the-art products. It broadly supports the development of projects in computer vision, natural language processing, reinforcement

learning and more.

It has a robust ecosystem and is supported on major cloud platforms.

Learning curve: Medium

PyTorch is easier to learn than other deep learning frameworks.

Adoption level: High

1900+ contributors on Github. Used by over 83,000 repositories on Github.

Who is using PyTorch?

salesforce, Stanford university, Udacity

Where to learn PyTorch

Learn the basics of PyTorch following these tutorials.

2. TensorFlow (TF)

machine_learning_libraries_tensorflow

TensorFlow is an open-source platform for machine learning developed by Google. TensorFlow was released to the public in November 2015. The core of TensorFlow is written in Python, C++, and CUDA.

TF is used both in research and production environment.

Although Python is widely used for TensorFlow, TensorFlow is available in R, JavaScript.

TF is popularly used for numerical computations. It has inbuilt machine learning and statistical tools. It is used to build projects on regression, classification, neural networks, and more.

In production, TensorFlow Extended (TFX) is used to build a production pipeline. It is optimized for large scaling and other deployment features.

Read more on MLOps.

TensorFlow has a visualization toolkit called TensorBoard. TensorBoard provides an interactive web-based dashboard for visualization.

TensorFlow can run computations on CPU, GPU, and TPU.

Learning curve: Medium

Adoption level: High

TensorFlow is widely used in production.

3,036+ Contributors; used by over 146,000 repositories on Github.

Examples

TensorFlow has a step-by-step example on its website.

Who is using TensorFlow?

Some of the companies using TensorFlow include;

Airbnb, Google, Coca-Cola, DeepMind, GE Healthcare, intel. Twitter, Dropbox, eBay, Lenovo, Linkedin, Nvidia, PayPal, Snapchat, Bloomberg, musical.ly, Kakao, AMD

Where to learn?

I recommend you start with TensorFlow tutorials here.

3. Scikit-learn

machine_learning_libraries_scikit_learn

Scikit-learn is a popular open-source machine learning library developed by David Cournapeau and initially released in June 2007.

Written in Python, C, C++, Cython.

Scikit-learn is not built to run across clusters. It is mainly used in experimentation.

learning curve: Easy

Adoption level: High

2000+ contributors on Github. Used by over 238,000 repositories on Github.

4. Spark MLlib

machine_learning_libraries_apache_spark_mllib

Apache Spark itself is a unified analytics engine for large-scale data processing. It is used for many things, including;

creating and managing data pipelines, data ingestion, data streams, machine learning modeling.

Spark MLlib is built on top of Spark. It is widely used in production because it integrates easily with other Spark components like Spark SQL, Spark Streaming.

learning curve: Medium

Adoption level: High

1600+ contributors on Github. Used by over 604 repositories on Github.

5. spaCy

machine_learning_libraries_spacy

spaCy is an open-source library developed by Explosion AI for natural language processing and written in Python and Cython.

spaCy is an excellent library for feature engineering and extracting information on text data. spaCy is built for production use, and it can handle large volumes of text data.

learning curve: Medium

Adoption level: Medium

542+ contributors on Github. Used by over 26,000 repositories on Github.

6. Natural language toolkit (NLTK)

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NLTK is an open-source library for natural language processing. It was initially developed by Steven Bird, Edward Loper, Ewan Klein.

Written in Python.

NLTK is very useful in preprocessing text data.

learning curve: easy

Adoption level: High

340 contributors on Github. Used by over 107,000 repositories on Github.

7. Numpy

machine_learning_libraries_numpy

NumPy is an open-source python library that offers an extensive collection of comprehensive mathematical functions. Numpy helps us work with arrays to perform various mathematical operations.

Written in Python and C.

Jim Hugunin created NumPy. Initially released in 1995.

learning curve: easy

Adoption level: High

1,169+ contributors on Github. Used by over 736,000 repositories on Github.

8. Pandas

machine_learning_libraries_pandas

Pandas is an open-source data analysis library. It is an excellent tool for data analysis and manipulation.

Pandas were created by Wes McKinney and released on 11 January 2008.

Written in Python, C, and Cython.

learning curve: easy

Adoption level: High

2380+ contributors on Github. used by over 469,000 repositories on Github.

9. Matplotlib

machine_learning_libraries_matplotlib_logo

Matplotlib is one of the most popular plotting open-source libraries for the Python programming language. It is used to plot a graphical representation of data.

learning curve: easy

Adoption level: High

1,097+ contributors, used by over 387,000 repositories on Github.

10. Keras

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Keras is an open-source library built on top of TensorFlow for creating an artificial neural network.

François Chollet originally developed it. Released in March 2015.

learning curve: medium

Adoption level: high

910+ contributors on Github.

Conclusion

These machine learning libraries and frameworks power a good number of machine learning products. They are widely used in state-of-the-art machine learning research and production.

They are;

  1. PyTorch
  2. TensorFlow
  3. Scikit-learn
  4. Spark MLlib
  5. spaCy
  6. NLTK
  7. Numpy
  8. Pandas
  9. Matplotlib
  10. Keras

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Jenniferhttps://jenniferkwentoh.com/about
My name is Jennifer Chiazor Kwentoh-Anionwu, and I am a Machine Learning Engineer. I build production-ready machine learning systems. When I'm not burning out my GPUs, I spend time painting beautiful portraits.

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