Machine Learning Libraries

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 dataset, reading of images, etc. This saves a lot of time for the developer and makes the life of the developer easier as the developers can directly use the functions of the libraries without knowing the implementation of the algorithms.

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, data frames. Python is a helpful language for data preparation, but it lags behind when it comes to data analysis and modeling. To overcome this lag, Pandas helps complete the entire data analysis workflow in Python without switching to any other domain-specific languages like R. Pandas enables the user to read/write datasets in various formats TEXT, CSV, XLS, JSON, SQL, HTML and many more. It gives high performance for data mining, reshaping, sub-setting, data alignment, slicing, indexing, 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, which is 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 useful for matrix data manipulation like reshape, transpose, fast mathematical/logical operations. Other operations like sorting, selecting, basic linear algebra, discrete Fourier transform and much more. NumPy consumes lesser memory and provides better runtime behaviour. But it is dependent on Cython, which makes NumPy difficult to integrate with other C/C++ libraries.

3. Matplotlib

Matplotlib is a data visualization library that works 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. The code for matplotlib may look daunting to some, but it is fairly easy to implement once the user gets used to it. But it takes a lot of practice to use matplotlib efficiently.

4. Sci-kit learn

Sci-kit learns can be considered as the heart of classical machine learning, which is completely focused on modeling the data instead of loading, manipulating or summarizing the data. Any task, you just name it, and sci-kit learn can perform it efficiently. One of the most simple and efficient libraries for data mining and data analysis, sci-kit learn is an open-source library that is 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 be used to prepare classification, regression, clustering, dimensionality reduction, model selection, feature extraction, normalization and much more. One drawback of sci-kit learn is, it is not convenient to utilize categorical data.

5. Seaborn

Seaborn library is built on top of the matplotlib. Seaborn makes it easy to plot data visualizations. It draws attractive 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 develop and train machine learning models. It is a widely accepted platform among ML researchers, developers, and production environments. Tensorflow performs various tasks, including model optimization, graphical representation, probabilistic reasoning, statistical analysis. Tensors are the basic concept of this library, which provides a generalization of vectors and matrices for high dimensional data. Tensorflow can do numerous ML tasks but is highly used to build deep neural networks.

7. Theano

Developed by Montreal Institute for learning algorithm (MILA), theano is a python library that enables the user 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, detects and resolves many errors.

8. Keras

‘Deep neural networks made easy’- that should be the tagline of this library. Keras is user-friendly designed for humans, which follows the best process to reduce the 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. A user can add new modules easily, which makes Keras suitable for high-level research. 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 the user to export models in the standard ONNX (Open Neural Network Exchange) to get direct access to ONNX platforms, runtimes and more.

10. OpenCV

OpenCV is a computer vision library that is built to provide central infrastructure for computer vision applications and improve machine perception. This library is free for commercial use. Algorithms provided by OpenCV can be used for face detection, object identification, track moving objects, and camera movements. 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 has the ability to 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. If you want to develop web applications, Flask can be the best python web application framework. 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, data. table, Caffe, NLTK, FastAI, Gluon and the list can go on and on.

Conclusion

So, this article gave an overview of current machine learning libraries, their uses, and some disadvantages too. We discussed various libraries that can perform a tedious tasks such as Matrix calculations, data mining, data visualization and face detection. However, you shouldn’t restrict yourself to these libraries. There are numerous awesome libraries available in the market.

Recommended Articles

This has been a guide to Machine Learning Libraries. Here we discuss the basic concept with different libraries of machine learning in a concise manner. You can also go through our other suggested articles to learn more –

1. Machine Learning Architecture
2. Types of Machine Learning
3. Careers in Machine Learning
4. Hyperparameter Machine Learning