The best open source Python machine learning libraries

Machine learning is a very fast and efficient technology developing in today’s world. In our society, humans are considered to have the most intelligent brains of all living things and can perform any task intelligently. Machine learning is a subset of AI (artificial intelligence) that is used to develop algorithms that can be used in computers to learn from previous data and history and make some meaningful decisions. The popularity of machine learning continues to grow over time because it can perform tasks that are complex for humans.

A few years ago, machine learning models were trained and coded manually using various algorithms and statistical concepts. This process is very time-consuming and inefficient. In recent days, training machine learning models has become easier, more efficient, and more productive. The reason behind this is the availability of many open source Python modules, frameworks, and libraries. Python is the most preferred programming language among developers due to its easy-to-understand syntax and wide range of available libraries. There are many Python libraries, such as Numpy, Pandas, Tensorflow, etc. In this article, you will learn about the top open source Python libraries for machine learning.

Best Machine Learning Open Source Library

The Chinese translation of

Numpy

is:

Numpy

Numpy is simply "Numerical Python". It is a very important Python library for machine learning research. It is a general-purpose package that you can use to process large arrays and multidimensional arrays. Various tools provided by Numpy include mathematical functions, linear algebra routines, and more. The advantage of Numpy is that it has the flexibility of Python and gains speed due to optimized compiled C code. Numpy's syntax is simple enough that any programmer can adopt it, regardless of their background.

Scipy

Scipy stands for "Scientific Python". It contains various modules for data optimization, integration and computational statistics. Scipy is built on NumPy. If you have the Scipy library installed, the Numpy extension will be automatically installed on your system. Scipy is very similar to MATLAB for big data processing. As we know Scipy is an open source library and there is an active and responsive community worldwide who are tasked with developing additional modules from time to time.

The translation of

Scikit-learn

is:

Scikit-learn

Scikit learn is a very popular Python library dedicated to classic machine learning algorithms. The library is built on Numpy and Scipy, two basic libraries of Python. To install the Scikit Learn library, you need to have the Numpy and Scipy libraries already installed on your system. Scikit Learn is supported for almost all learning algorithms, whether supervised or unsupervised. Scikit learn library in Python is used for data mining and data analysis. This feature makes this library stand out among newcomers to machine learning.

Theano

As we all know, machine learning trains models by using mathematical and statistical methods. Theano is a very famous open source Python library that can be used for various operations such as defining, evaluating and optimizing complex mathematical expressions, including multi-dimensional arrays. The Theano library achieves this efficiency by manipulating and optimizing distributed usage of CPUs and GPUs. This library is specially designed for unit testing and verification and can be used to detect any kind of errors.

TensorFlow

Tensor is an open source Python library developed by researchers at “Google”. The TensorFlow library is used to perform complex numerical calculations to achieve higher performance efficiency. Tensorflow consists of defining and running calculations involving tensors. It is also used to run some deep neural networks, which are used in various artificial intelligence-based application development. Using tensorflow, we can create a data flow graph that shows the movement of data on that particular graph.

KRAS

Keras is a very popular high-level deep learning API developed by Google. This library is used for the implementation of machine learning neural networks. The basic source code of this library is written in Python language and allows easy implementation of neural networks. The Keras library is relatively easy to learn and use. This is because the front-end of this library is the Python language, which has high abstraction precision and supports various back-end calculations. This is why the Keras library is slightly slower than other machine learning frameworks. With Keras, you can switch between various backends, which makes the library beginner-friendly.

PyTorch

PyTorch is an open source Python library for machine learning. This library supports a variety of tools for natural language processing (NLP), computer vision, and many other machine learning tools. Using this library, developers can perform calculations on various tasks or tensors and perform GPU acceleration. It also allows developers to create a graph to showcase their calculations.

The Chinese translation of

Pandas

is:

Pandas

The Pandas library was developed by Wes McKinney in 2008. This library is built on top of the Numpy library. Pandas is a library in Python programming that supports various data structures and operations to enable efficient manipulation of numerical data and time series. The library provides various methods to group, merge, and filter datasets.

The Chinese translation of

Matplotlib

is:

Matplotlib

Matplotlib is an open source Python library for data visualization. The Matplotlib library is also used to create 2D graphs and plot data on graphs. Some of the features of this library include controlling line styles, formatting, and more. The library supports many kinds of graphics, such as histograms, histograms, etc., for data visualization.

in conclusion

• The popularity of machine learning has increased over time because it can perform tasks that are complex for humans.

• Various open source Python libraries enable the developer community to build machine learning models in less time and more efficiently than manually built machine learning models.

• Some of the top open source Python libraries for machine learning are Numpy, Matplotlib, Scipy, Pandas, Tensorflow, etc.

• Numpy has an advantage among developers because it has the flexibility of Python and gains speed due to optimized compiled C code.

• Pandas is a package library in Python programming that supports various data structures and operations and can efficiently complete numerical data operations and time series operations.

• TensorFlow is used to run some deep neural networks that are used to develop various artificial intelligence-based applications.

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