What are Some of the Best Python Libraries for Machine Learning?

When we hear the word library, the first thing that comes to mind is a silent room full of bookshelves where we read the book among so many different kinds of books. So let’s compare this analogy with the libraries of Python for machine learning. As you will get categories of books in the virtual library, likewise in the python library, you will have a list of modules where software programmers figure out the requirements and then search for the machine learning for Python to import the modules that are suited best.

When it comes to choosing a Python Machine learning, here is a list of questions that people are looking for

What are the Python best Libraries for Machine Learning?

Let’s have a look at some of the best libraries of Python for machine learning.

1. NumPy Python:

It is a library used for processing Python NumPy array consisting of highly complex mathematical functions, which make it powerful enough to deal with substantial multi-dimensional matrices. It is easy to use and interact with as it supports mathematical operations and makes the calculations very simple.

Developed by – Travis Oliphant

Launched year -2005

Github –https://github.com/numpy/numpy

2. Tensor flow python:

TensorFlow is an open-source Python library that defines and runs the series of operations on tensors used for numerical computations. Tensors are the matrices that represent your data. It runs and trains neural networks, which are further used in AI applications.

It was developed by – Google Brain team of Google

Launched year -2015

Github – https://github.com/tensorflow/tensorflow

Written in – Python, CUDA, and C++

3. Python Scikit-Learn

Python Scikit-learn is considered one of the best Python libraries for Machine Learning which lets you utilize more than a single metric. When it comes to deal with Python heavily, Scikit provides the most efficient way for complex data. It is considered as the top-notch library that provides adequate tools for ML.

Developed by – David Cournapeau

Launched year -2007

Github – https://github.com/scikit-learn/scikit-learn

Written in – Python, C, C++, and Cython

4. Theano Python

Theano Python undergoes computations quicker than CPU. It efficiently recognizes and diagnoses errors. It provides tools that involve defining, executing, and optimizing mathematical models and expressions with multi-dimensional arrays.

It was Developed by – Montreal Institute for Learning Algorithms (MILA), University of Montreal

Launched year -2007

Github – https://github.com/Theano/Theano

Written in – Python, CUDA

5. Python SciPy

Python SciPy efficiently uses NumPy arrays to generate data structures as it supports NumPy.lib.scimath and efficiently manages 1-D polynomials in two different systems, which helps provide faster computational power.

It was developed by – Community library project

Launched in the year -2001

Github – https://github.com/scipy/scipy

Written in – Python, C++, C, and Fortran

6. Keras Python

Keras Python works similarly on CPU and GPU as it mainly supports all the Neural Networks models. Keran Python is very flexible and easy to utilize because it helps multi-backend and contains modular architecture.

Developed by – François Chollet

Launched year -2015

Github – https://github.com/keras-team/keras

Written in – Python

7. Python PyTorch

Python PyTorch can be efficiently utilized with other libraries and Python machine learning packages. It offers immense flexibility and performance optimization in both research and production environments, providing a robust ecosystem.

Developed by – Facebook’s AI Research lab

Launched year -2016

Github – https://github.com/pytorch/pytorch

Written in – Python, CUDA, and C++

8. Python Pandas

Python Pandas is the best tool for data analysis and manipulation as it provides support to multiple operations like

✦ Aggregations

✦ Visualizations

✦ Concatenations

✦ Iteration

✦ Sorting

Developed by – Wes McKinney

Launched year – 2008

Github – https://github.com/pandas-dev/pandas

Written in – Python, Cython, and C

9. Python Matplotlib

Python Matplotlib provides comprehensive and robust tools for plotting that allow you to analyze data in a much more detailed way properly.

Developed by – Michael Droettboom, et al.

Launched year -2003

Github –https://github.com/matplotlib/matplotlib

Written in – Python

Conclusion

To conclude, these were some of the best python machine learning libraries from which you can accomplish large scale project requirements.