Machine Learning is a very fast and efficient growing technology in the current world. In our society, human beings are considered the most intelligent brains among all living beings to perform any task smartly. Machine learning is the subset of AI (Artificial Intelligence), which is used to develop algorithms that can be used in a computer to learn from previous data and history and make some meaningful decisions. The popularity of machine learning is increasing with time because machine learning can perform tasks that are complex for a human being.

A few years ago, the training and coding of a machine learning model manually by using a variety of algorithms and statistical concepts. This process was very time taking and also not so efficient. In recent days, training a machine learning model has become easy, 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 its wide range of available libraries. There are various Python libraries such as Numpy, Pandas, Tensorflow, etc. In this article, you will get through the top open-source Python libraries for machine learning one by one.

Best open-source Libraries for Machine Learning

Numpy

Numpy simply means “Numerical Python”. It is a very important Python library for the study of machine learning. It is a general-purpose package using which you can process a large number of arrays and multidimensional arrays. The various tools provided by the Numpy are mathematical functions, linear algebra routines, etc. Numpy got an advantage because it has the flexibility of Python and it got speed due to optimized compiled C codes. The syntax of Numpy is very easy and can be adopted by any programmer, irrespective of their background.

Scipy

Scipy stands for “Scientific Python”. In it, there are various modules for data optimization, integration, and computational statistics. Scipy is built on top of NumPy. If you install the Scipy library, the Numpy extension will automatically get installed in your system. Scipy is very similar to MATLAB, which is used for large data processing. As we know, Scipy is an open-source library, it has an active and quickly responsive community over the globe whose task is to develop additional models from time to time.

Scikit-learn

Scikit learn is a very popular Python library that is specifically used for the classical machine learning algorithm. This library is built above the two very basic libraries of Python which are Numpy and Scipy. To install the Scikit Learn library, you need two libraries Numpy and Scipy already installed on your system. For almost all of the learning algorithms either supervised or unsupervised learning algorithms, Scikit Learn is supported. Scikit learn library in Python is used for both data mining and data analysis. This feature makes this library stand out among the freshers in machine learning.

Theano

As we know, machine learning is all about training models through the use of mathematical and statistical approaches. 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. Theano library achieves this kind of efficiency by manipulating and optimizing the distributed use of CPU and GPU. This library is specifically used for unit testing and verification that can be used to detect any kind of error.

TensorFlow

Tensor is an open-source Python library that was developed by researchers at ‘Google’. The TensorFlow library is used for doing complex numerical computations to achieve higher performance efficiency. The tensor flow consists of defining and running calculations in which tensors are involved. It is also used for running some deep neural networks that are used in various AI-based application development. Using tensor flow, we can create a data flow graph to show the movement of data on that particular graph.

Keras

Keras is a very popular high-level, deep-learning API that was developed by Google. This library is used in the implementation of neural networks of machine learning. The basic source code of this library was written in Python language, making it easy to implement neural networks. Keras Library is comparatively easy to learn and work with. This is because the front end of this library is in Python language with a high precision of abstraction that supports various backend computations at the same time. This is the reason why the Keras library is a little slower than other machine learning frameworks. Using Keras, you can switch various backends which makes this library beginner friendly.

PyTorch

PyTorch is an open-source Python library for machine learning. There is a wide range of tools supported in this library that are used for Natural Language Processing (NLP), computer vision, and many other machine learning tools. Using this library, a developer can compute various tasks or tensors along with GPU acceleration. It also allows developers to create a graph to show their computations.

Pandas

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 so that manipulation of numerical data and manipulation of time series can be done efficiently. There are various methods available in this library to the group, combine, and filter data from datasets.

Matplotlib

Matplotlib is an open-source Python library that is used in the implementation of data visualization. Matplotlib library is also used to create 2D graphs and plot data over the graph. Some of the features of this library are control line styles, formatting excesses, etc. There are many kinds of graphs supported in this library such as bar charts, histograms, and others to implement data visualization.

Conclusion

• The popularity of machine learning is increasing over time because machine learning can perform tasks that are complex for a human being.

• Various open-source Python libraries make it easy for the developers’ community to build machine learning models in less time, and also it is more efficient than manually built machine learning models.

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

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

• Pandas is a package library in Python programming that supports various data structures and operations so that manipulation of numerical data and manipulation of time series can be done efficiently.

• TensorFlow is used for running some deep neural networks that are used in the development of various AI-based applications.