Updated on: January 23, 2023
Python is one of the most popular programming languages that is being used to develop machine learning and deep learning models. Nowadays, Python’s library is being used to develop applications based on artificial intelligence, machine learning, and deep learning models. Let’s have a look at top python libraries for machine learning
Usually, AI-based projects are different from conventional projects developed by developers. For such projects, there is a need for a suitable programming language that is completely efficient in making the application stable and extensible. Thus, here, Python uses its extensive set of libraries, which help in performing the development tasks efficiently and effectively.
What are the benefits of using Python?
Why is Python used for such development tasks? Well, this can be understood with the help of the following benefits that Python brings in:
- It is simple and compatible. Its simplicity and the libraries and frameworks it uses make it suitable for developing reliable systems.
- It works as a platform-independent feature. It can work on any platform, including Windows, Linux, Mac OS, etc.
- It is supported by a large community. Its community offers both new and experienced developers the necessary support system.
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Top 10 Python libraries for machine learning
With the increase in demand for smart products, machine learning is on the rise. It had taken the place of being the most prominent cost-cutting tool in almost every industry these days. The libraries that are helpful for the development of machine learning models are available in several programming languages, but with Python, things are more user-friendly and easier to manage. There are several libraries in Python that can be helpful for machine learning.
Some of the python libraries for machine learning are:
1. Tensorflow:
it is the library developed by the Google Brain team for deep learning and neural networks. It helps in the easy distribution of work among CPU cores or GPU cores. Tensors are used in this library for this purpose.
2. NumPy:
it is a fast computational library that is known for handling multiple tasks and functions such as basic algebra; fourier transforms, random simulations, and even shape manipulations. Generally based on the older library numeric, this library is used for handling multi-dimensional data and intricate mathematical functions.
3. Natural Language Toolkit:
It is the most widely used library among others that is used for text classification and natural language processing. It can be used for stemming, tokenization, and searching for a keyword in a document, along with sentiment analysis, text-classifiers, etc.
4. Pandas:
This library belongs to Python’s web framework and is used for data manipulation meant for numerical data and time series. It makes use of data series and frames to define 3-dimensional and 2-dimensional data, as well as provide help in indexing large data sets for quick search.
5. Scikit-learn:
it is the library written on top of NumPy, SciPy, and Matplotlib. It is based on several data modeling concepts, such as regression, clustering, classification, model selection, etc. It can easily be integrated with other machine learning libraries.
6. Keras:
This library is intended to provide a Python-based tensorflow interface that is entirely focused on AI neural networks. It contains standard blocks of common neural networks and the tools that help make the images and text processing faster and smoother.
7. PyTorch:
it is the Facebook-developed machine learning library that is written in Python for web development. The main focus of this library is to develop and train deep learning models. Along with Python, this library had extensions in both the C and C++ languages.
8. mLPack:
it is a C++-based machine learning library that binds Python with other languages such as R programming, Julia, and GO. This library supports all ML algorithms such as K-means, least-angle regression, etc. It is a fast, scalable, and easy-to-understand library. It comes with a BSD license that allows it to be used as both open-source and proprietary software.
9. OpenCV:
it is an open-source platform that is meant for computer vision and image processing. For this, it had over 2500 algorithms. It can aid in the tracking of human movements, the extraction of 3D models, and the stitching of images together to create high-resolution images. It is used in several CCTV monitoring activities in many countries.
10. Matplotlib:
Matplotlib is used in Python to create the graphical representation for better understanding of the data. It makes use of the Python GUI toolkit, which can help in producing graphs and plots by using object-oriented APIs. This library is known for providing a MATLAB-like interface that can process similar things and tasks as MATLAB. It is a free and open-source library.
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Aside from the aforementioned, Python had a large number of libraries to aid in development tasks. Every library had its own advantages and disadvantages. Before selecting the library, one should check out the purpose of the machine learning and the model’s accuracy. Proper testing and training of the models should be done in the meantime to avoid any errors thereafter.
