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7 Amazing Machine learning Python Libraries for 2020

kris
React native developer and Technical writer Americano addicted
Originally published at Medium on ・7 min read

Machine learning is the most algorithm-intense field in computer science. Those days when people had to code all algorithms for machine learning. Thanks to Python and it’s libraries, modules, and frameworks.

Python machine learning libraries have become the preferred language for implementing machine learning algorithms. Let’s take a look at the main Python libraries used for machine learning.

Amazing Machine learning Python Libraries

1.SciPy

SciPy.org - SciPy.org

With machine learning growing at supersonic speed, many Python developers were creating python libraries for machine learning, especially for scientific and analytical computing. Travis Oliphant, Eric Jones, and Pearu Peterson in 2001 decided to merge most of these bits and pieces codes and standardize it. The resulting library was then named as SciPy library.

The current development of the SciPy library is supported and sponsored by an open community of developers and distributed under the free BSD license.

The SciPy library offers modules for linear algebra, image optimization, integration interpolation, special functions, Fast Fourier transform, signal and image processing, Ordinary Differential Equation (ODE) solving, and other computational tasks in science and analytics.

The underlying data structure used by SciPy is a multi-dimensional array provided by the NumPy module. SciPy depends on NumPy for the array manipulation subroutines. The SciPy library was built to work with NumPy arrays along with providing user-friendly and efficient numerical functions.

For more about SciPy library

2.NumPy

NumPy - NumPy

NumPy is a well known general-purpose array-processing package. A large collection of high complexity mathematical functions make NumPy powerful to process large multi-dimensional arrays and matrices. NumPy is very useful for handling linear algebra, Fourier transforms, and random numbers. Other libraries like TensorFlow uses NumPy at the backend for manipulating tensors.

With NumPy, you can define arbitrary data types and easily integrate with most databases. NumPy can also serve as an efficient multi-dimensional container for any generic data that is in any datatype. The key features of NumPy include powerful N-dimensional array object, broadcasting functions, and out-of-box tools to integrate C/C++ and Fortran code.

For more about Numpy library

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3.Keras

keras-team/keras

Keras has over 200,000 users as of November 2017. Keras is an open-source library used for neural networks and machine learning. Keras can run on top of TensorFlow, Theano, Microsoft Cognitive Toolkit, R, or PlaidML. Keras also can run efficiently on CPU and GPU.

Keras works with neural-network building blocks like layers, objectives, activation functions, and optimizers. Keras also have a bunch of features to work on images and text images that comes handy when writing Deep Neural Network code.

Apart from the standard neural network, Keras supports convolutional and recurrent neural networks.

For more about Keras library

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4.Matplotlib

Matplotlib: Python plotting - Matplotlib 3.1.3 documentation

Matplotlib is a data visualization library that is used for 2D plotting to produce publication-quality image plots and figures in a variety of formats. The library helps to generate histograms, plots, error charts, scatter plots, bar charts with just a few lines of code.

It provides a MATLAB-like interface and is exceptionally user-friendly. It works by using standard GUI toolkits like GTK+, wxPython, Tkinter, or Qt to provide an object-oriented API that helps programmers to embed graphs and plots into their applications.

For more about Matplotlib library

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5.PyTorch

pytorch/pytorch

PyTorch has a range of tools and libraries that support computer vision, machine learning and natural language processing. The PyTorch library is open source and based on the Torch library. The main advantage of the PyTorch library is that it is easy to learn and use.

PyTorch can be easily integrated into the Python Data Science stack, including NumPy. You will hardly see a difference between NumPy and PyTorch. With PyTorch, developers can also perform tensor calculations. PyTorch has a robust framework with which you can create calculation diagrams on the go and even modify them at runtime. Other benefits of PyTorch include support for multiple GPUs, simplified preprocessors, and custom data loaders.

For more about PyTorch library

6.Theano

Theano/Theano

Theano is a Python machine learning library that can act as an optimization compiler to evaluate and edit mathematical expressions and matrix calculations. Theano is based on NumPy and is tightly integrated with NumPy. The user interface is very similar. Theano can work with graphics processors (GPU) and CPU.

Working on the GPU architecture leads to faster results. Theano can perform data-intensive calculations on a GPU up to 140 times faster than on a CPU. Theano can automatically avoid errors and bugs when it comes to logarithmic and exponential functions. Theano has built-in unit testing and validation tools to avoid errors and problems.

For more about Theano library

7.Pandas

pandas

Pandas are proving to be the most popular Python library for data analysis with the support of fast, flexible and expressive data structures that have been developed for both “relational” and “labeled” data. Pandas is now an important library for solving the analysis of practical and real data in Python. Pandas is very stable and offers a highly optimized performance. The backend code is written in C or Python.

The two main types of data structures used by pandas are :

  • Series (1-dimensional)
  • DataFrame (2-dimensional)

These two put together can handle a vast majority of data requirements and use cases from most sectors like science, statistics, social, finance, and of course, analytics and other areas of engineering.

Top 5 courses to learn Pandas and Python Data Analysis in 2020 - Best of Lot

Pandas support and perform well with different kinds of data including the below :

  • Tabular data with columns of heterogeneous data. For instance, consider the data coming from the SQL table or Excel spreadsheet.
  • Ordered and unordered time series data. The frequency of time series need not be fixed, unlike other libraries and tools. Pandas is exceptionally robust in handling uneven time-series data
  • Arbitrary matrix data with the homogeneous or heterogeneous type of data in the rows and columns
  • Any other form of statistical or observational data sets. The data need not be labeled at all. Pandas data structure can process it even without labeling.

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Conclusion

Python is the go-to language when it comes to data science and machine learning and there are multiple reasons to choose python for data science.

Python has an active community that most developers create libraries for their own purposes and later release it to the public for their benefit. Here are some of the common machine learning libraries used by Python developers.

It’s a great chance for the programmer, especially those who have good knowledge of maths and statistics to make a career in machine learning and data science. You will be awarded exciting work and incredible pay.

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Closing Notes

Thanks, You made it to the end of the article … Good luck with your Data Science and Machine Learning journey! It’s certainly not going to be easy, but by following these courses, you are one step closer to becoming the Machine Learning Specialists you always wanted to be.


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