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Dev Duniya

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10 Best Python Libraries for Machine Learning You Should Know in 2022 - DevDuniya

Python is one of the most popular programming language for Machine Learning Projects. And on the other side, Machine Learning is a leading field in the 21st century. To grasp machine learning or Artificial Intelligence, you need to know complete python programming. Python Programming is used in Machine Learning, Deep Learning and Artificial Intelligence is becoming popular day by day. Python programming is a simple and easy programming language.

In this article, We are going to introduce the Top 10 Most Popular Python Libraries for Machine Learning. We have listed the best Python libraries for machine learning for beginners. If you want to be a master of machine learning then you will have to learn these python libraries, so let’s start.

1. Pandas

Pandas is mainly used for data analysis, which is one of the most popular Python libraries. Pandas library is developed for extracting and organizing data and it also offers built-in functions and methods to the group, combine, and filter datasets. Data can be prepared, loaded, manipulate and analyze with the panda’s library. Pandas library has powerful data frames, flexible data handling, and low-level python libraries. Some of the features of Pandas Library are:- Aggregations, Visualizations, Concatenations, Iteration and Sorting, etc.
Developed by Wes Mckinney
Launched in – 2008
Written in Python, C, and Cython

2. NumPy

NumPy stands for Numerical Python used to process the python NumPy array and support n-dimensional arrays. NumPy Library is used to handle linear algebra and Fourier series transformations. NumPy is used by Tensorflow and other libraries for their internal calculations. NumPy has many built-in functions for numerical routines that make it so popular. Some of the features of Numpy Libraries are:- Easy to use and interact, Enhances performance, Has a large community of programmers and Manages garbage collection, etc.
Developed by Travis Oliphant
Launched in – 2005
Written in Python, C, and Cython

3. Scikit-Learn

Scikit Learning is one of the best Python machine Learning Libraries for building Machine Learning Algorithms. It was first introduced as a third-party extension to the Scipy Library. Scikit Learn Library can be used to build machine learning models because it contains a large number of tools for predictive modeling and analysis. Some of the features of Scikit Learning Libraries are:- contains massive potential algorithms, fast community support, etc.
Developed by David Cournapeau
Launched in – 2007
Written in– Python, C++, and Cyhton

4. Matplotlib

Matplotlib is one of the best python libraries used for data visualization. It is used to draw patter or graphs because it offers lots of features and tools controlling line style, font properties, and many more. It is one of the top machine learning libraries to explore. You can create Bar Charts, Error Charts, Histograms and scatter plots, etc. The main use of Matplotlib is Data Visualization and plotting.
Developed by– Michael Droettboom et al
Launched in – 2003
Written in– Python

5. SciPy

SciPy stands for Scientific Python which is an open-source python library. SciPy is used in Scientific computing and mathematical functions derived from NumPy. There are many features of the SciPy Library, as it-is-easy to use the library, fast computational power, and improved computations. SciPy Python Library is used for Scientific computation and technical computing. Some Features of SciPy are Fast Fourier transform, image optimization, linear algebra, etc.
Developed by– Community library project
Launched in – 2001
Written in– Python, C++, C, and Fortran

6. TensorFlow

TensorFlow is one of the most popular Machine Learning Libraries based on neural networks. TensorFlow was created by the Google Brain research team in 2015 to use in google. But after some time it started to gain a lot of popularity among big companies and now it is one of the top python machine learning libraries. TensorFlow is an
open-source library used for numerical computation and it runs and trains neural networks. TensorFlow uses a multidimensional array known as ten

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