Updated May 23, 2023
Introduction to Machine Learning Libraries
Machine Learning libraries (Pandas, Numpy, Matplotlib, OpenCV, Flask, Seaborn, etc.) are defined as an interface of a set of rules or optimized functions that are written in a given language to perform repetitive work like arithmetic computation, visualizing datasets, reading of images, etc. This saves a lot of time for the developer and makes the developer’s life easier as the developers can directly use the libraries’ functions without knowing the algorithms’ implementation.
Libraries of Machine Learning
Following are some of the most popular Machine Learning Libraries:
- Pandas
- Numpy
- Matplotlib
- Scikit learn
- Seaborn
- Tensorflow
- Theano
- Keras
- PyTorch
- OpenCV
- Flask
Let’s get to know them in a nutshell!
1. Pandas
Pandas is an open-source Python library that provides flexible, high-performance, and easy-to-use data structures like series and data frames. Python is a helpful language for data preparation but lags in data analysis and modeling. To overcome this lag, Pandas helps complete the entire data analysis workflow in Python without switching to other domain-specific languages like R. Pandas enables users to read/write datasets in various formats TEXT, CSV, XLS, JSON, SQL, HTML, and many more. It performs highly for data mining, reshaping, sub-setting, data alignment, slicing, indexing, and merging/joining data sets. But pandas are inefficient when it comes to memory utilization. It creates too many objects to make data manipulation easy, which utilizes high memory.
2. NumPy
NumPy is the most fundamental data handling library, popularly used for scientific computing with Python. It allows the user to handle a large N-dimensional array with the ability to perform mathematical operations. NumPy is famous for its runtime execution speed, parallelization, and vectorization capabilities. It is helpful for matrix data manipulation like reshaping, transposing, and fast mathematical/logical operations. Other operations include sorting, selecting, basic linear algebra, discrete Fourier transform, and more. NumPy consumes lesser memory and provides better runtime behavior. But it depends on Cython, making NumPy difficult to integrate with other C/C++ libraries.
3. Matplotlib
Matplotlib is a data visualization library with numpy, pandas, and other interactive environments across platforms. It produces high-quality visualization of data. Matplotlib can be customized to plot charts, axis, figures, or publications, and it is easy to use in jupyter notebooks. Once the user becomes familiar with it, implementing the code for matplotlib is pretty easy, although it may appear daunting to some. But it takes a lot of practice to use matplotlib efficiently.
4. Sci-kit learn
Sci-kit learning is the heart of classical machine learning, which is completely focused on modeling the data instead of loading, manipulating, or summarizing the data. You name any task, and sci-kit learns you can perform it efficiently. One of the most simple and efficient libraries for data mining and analysis, sci-kit learn is an open-source library built on NumPy, SciPy & Matplotlib. It was developed as a part of the google summer code project, which now has become a widely accepted library for machine learning tasks. Sci-kit learns can prepare classification, regression, clustering, dimensionality reduction, model selection, feature extraction, normalization, etc. One drawback of sci-kit learning is it is not convenient to utilize categorical data.
5. Seaborn
The seaborn library is built on top of the matplotlib. Seaborn makes it easy to plot data visualizations. It draws pretty information-generating graphs with fewer lines of code. Seaborn has special support for categorical and multivariate data to show aggregate statistics.
6. Tensorflow
Developed by the Google brain team for its internal use, TensorFlow is an open-source platform to build and train machine learning models. ML researchers, developers, and production environments widely accept and utilize Sci-kit Learn as a prominent platform. Tensorflow performs various tasks, including model optimization, graphical representation, probabilistic reasoning, and statistical analysis. Tensors are the basic concept of this library, which provides a generalization of vectors and matrices for high-dimensional data. People use TensorFlow to build deep neural networks and perform numerous machine learning tasks.
7. Theano
Developed by Montreal Institute for Learning algorithm (MILA), theano is a Python library that enables users to evaluate mathematical expressions with N-Dimensional arrays. Yes, this is similar to the Numpy Library. The only difference is Numpy is helpful in machine learning, while theano works well for deep learning. In addition, Theano provides faster computational speed than a CPU and detects and resolves many errors.
8. Keras
‘Deep neural networks made easy’ should be this library’s tagline. Keras is user-friendly and designed for humans, which follows the best process to reduce cognitive load. Keras provides easy and fast prototyping. It is a high-level neural networks API written in Python and runs on top of CNTK, TensorFlow, and MXNET. Keras provides a large number of already pre-trained models. It supports recurrent and convolutional networks and the combination of both networks too. Users can easily add new modules, making Keras suitable for high-level research. The performance of Keras completely depends on under-the-hood backends (CNTK, TensorFlow, and MXNET)
9. PyTorch
PyTorch was initially developed by Facebook’s artificial intelligence team, which later combined with caffe2. Till TensorFlow came, PyTorch was the only deep learning framework in the market. It is so integrated with Python that it can be used with other trending libraries like numpy, Python, etc. Furthermore, PyTorch allows users to export models in the standard ONNX (Open Neural Network Exchange) to directly access ONNX platforms, runtimes, and more.
10. OpenCV
OpenCV is a computer vision library built to provide central infrastructure for computer vision applications and improve machine perception. This library is free for commercial use. OpenCV provides applicable algorithms for various tasks such as face detection, object identification, tracking moving objects, and camera movement analysis. In addition, OpenCV is useful for combining two images, which can produce high-resolution images, follow eye movements, extract 3D models of objects, and much more. It can perform on different platforms; its C++, Java, and Python interfaces can support Windows, macOS, iOS, Linux, and Android.
11. Flask
A group of international Python enthusiasts developed a flask in 2004. The Flask can be the best Python web application framework if you want to develop web applications. It relies on the Jinja template engine and the Werkzeug WSGI toolkit. It is compatible with the Google app engine and contains the development server and debugger. Some other libraries:- Scrapy, Plotly, Bokeh, Spacy, Dask, Gensim, and Data. Table, Caffe, NLTK, FastAI, Gluon, and the list can continue.
Conclusion
So, this article gave an overview of current machine learning libraries, their uses, and some disadvantages. We discussed various libraries that can perform tedious tasks such as Matrix calculations, data mining, data visualization, and face detection. However, it would be best if you didn’t restrict yourself to these libraries. There are numerous excellent libraries available in the market.
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