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Table of contents
1.
Introduction
2.
TensorFlow
3.
Keras
4.
Pandas
5.
Matplotlib
6.
PyTorch
7.
Scikit-Learn
8.
Scipy
9.
Theano
10.
Caffe2
11.
Frequently Aske Questions
11.1.
1. What does reversed function in python do?
11.2.
2. What is an interpreted language?
11.3.
3. What are the applications of python?
11.4.
4. What are Python literals?
11.5.
5. How are parameters passed in Python functions by default?
12.
Conclusion
Last Updated: Mar 27, 2024
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Machine learning Libraries

Author Anant Dhakad
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Introduction

Python comes with tons of libraries and inbuilt functions. There are libraries for data science, machine learning, maths etc. Often, these inbuilt functions come in handy when doing a specific task in Python. In this blog, we will learn about the different libraries in machine learning.

TensorFlow

TensorFlow is a Google-developed software library (framework) for rapidly implementing machine learning and deep learning principles. It combines computational algebra and optimization approaches to make many mathematical equations simple to calculate.

It has a feature that uses multi-dimensional arrays called tensors to design, optimise, and calculate mathematical expressions. It also includes high scalable feature for computation with various datasets.

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Keras

Keras is a Machine Learning framework built on top of open source frameworks such as TensorFlow, Theano, and Cognitive Toolkit (CNTK). Theano is a Python library for doing fast numerical computations.   TensorFlow is extremely adaptable, and its main advantage is distributed computing. Microsoft created the CNTK deep learning framework. It makes use of libraries like Python, C#, and C++, as well as standalone machine learning toolkits. Theano and TensorFlow are great libraries for building neural networks, but they are difficult to understand.

You can also practice with the help of Online Python Compiler

Pandas

Pandas is a Python package that allows you to work with large data sets. It offers tools for data analysis, cleansing, exploration, and manipulation. Pandas makes it possible to evaluate large amounts of data and provide conclusions based on statistical theory. Pandas can clean up and produce readable and useful data collections.

Matplotlib

One of the most widely used Python packages for data visualisation is Matplotlib. It's a cross-platform library that generates 2D charts from array data. Matplotlib is a Python library that uses NumPy, Python's numerical mathematics extension. It provides an object-oriented API for embedding plots in Python GUI toolkits such as PyQt and WxPythonotTkinter applications. It's also compatible with Python and IPython shells, as well as Jupyter notebooks and web application servers.

PyTorch

PyTorch is a Python machine learning package that is free source. It's utilised in a variety of applications, including natural language processing. It was created by Facebook's artificial intelligence research team, and Uber's Pyro probabilistic programming engine is based on it.

Scikit-Learn

In Python, Scikit-learn (Sklearn) is the most usable and robust machine learning library. It uses a Python consistency interface to give a set of efficient tools for machine learning and statistical modellings, such as classification, regression, clustering, and dimensionality reduction. NumPy, SciPy, and Matplotlib are the foundations of this Python-based library.

Scipy

SciPy is a scientific computation package built on top of NumPy. Scientific Python (SciPy) is a Python programming language. It has greater optimization, statistics, and signal processing functions. SciPy, like NumPy, is open source, which means we can use it without restriction.

Theano

Theano is a Python package that allows you to write mathematical expressions for Machine Learning, optimise them, and evaluate them quickly by utilising GPUs in important areas. In most circumstances, it can compete with complete C implementations.

Caffe2

The Berkeley Vision and Learning Center developed Caffe (Convolutional Architecture for Fast Feature Embedding), a deep learning platform (BVLC). Yangqing Jia founded the Caffe project while working on his Ph.D. at the University of California, Berkeley. Caffe is a simple tool for experimenting with deep learning. It's written in C++ and has Python and Matlab bindings.

Also See, Intersection in Python and Convert String to List Python.

Also Read, Divmod in Python

Frequently Aske Questions

1. What does reversed function in python do?

The reversed() function allows us to reverse the order of items in a sequence. It takes a sequence as input and returns an iterator.

2. What is an interpreted language?

The statements in an interpretable language are executed line by line. Interpreted languages include Python, Javascript, R, PHP, and Ruby. An interpreted language program executes directly from the source code, without the need for a compilation phase.

3. What are the applications of python?

Python is used for many purposes. Common applications include:

  • Web development
  • Game development
  • Data Science
  • Machine Learning

4. What are Python literals?

Literals are data that is contained within a variable or constant. Python supports different literals including strings, numerical and boolean literals

5. How are parameters passed in Python functions by default?

By default, parameters are passed by reference in Python functions.

Conclusion

Cheers if you reached here!! 

In this article, we learnt about different machine learning libraries in python.

We hope that this blog has helped you enhance your knowledge regarding machine learning libraries in python and if you would like to learn more, check out our articles on the platform Coding Ninjas Studio. You can also consider our Machine Learning Course to give your career an edge over others.

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