Machine learning is the most algorithm-intense field in computer science. Gone are those days when people had to code all algorithms for machine learning. Thanks to Python and it’s libraries, modules, and frameworks.
Python machine learning libraries have grown to become the most preferred language for machine learning algorithm implementations. Let’s have a look at the main Python libraries used for machine learning.
Top Python Machine Learning Libraries
NumPy is a well known general-purpose array-processing package. An extensive collection of high complexity mathematical functions make NumPy powerful to process large multi-dimensional arrays and matrices. NumPy is very useful for handling linear algebra, Fourier transforms, and random numbers. Other libraries like TensorFlow uses NumPy at the backend for manipulating tensors.
With NumPy, you can define arbitrary data types and easily integrate with most databases. NumPy can also serve as an efficient multi-dimensional container for any generic data that is in any datatype. The key features of NumPy include powerful N-dimensional array object, broadcasting functions, and out-of-box tools to integrate C/C++ and Fortran code.
With machine learning growing at supersonic speed, many Python developers were creating python libraries for machine learning, especially for scientific and analytical computing. Travis Oliphant, Eric Jones, and Pearu Peterson in 2001 decided to merge most of these bits and pieces codes and standardize it. The resulting library was then named as SciPy library.
The current development of the SciPy library is supported and sponsored by an open community of developers and distributed under the free BSD license.
The SciPy library offers modules for linear algebra, image optimization, integration interpolation, special functions, Fast Fourier transform, signal and image processing, Ordinary Differential Equation (ODE) solving, and other computational tasks in science and analytics.
The underlying data structure used by SciPy is a multi-dimensional array provided by the NumPy module. SciPy depends on NumPy for the array manipulation subroutines. The SciPy library was built to work with NumPy arrays along with providing user-friendly and efficient numerical functions.
In 2007, David Cournapeau developed the Scikit-learn library as part of the Google Summer of Code project. In 2010 INRIA involved and did the public release in January 2010. Skikit-learn was built on top of two Python libraries – NumPy and SciPy and has become the most popular Python machine learning library for developing machine learning algorithms.
Scikit-learn has a wide range of supervised and unsupervised learning algorithms that works on a consistent interface in Python. The library can also be used for data-mining and data analysis. The main machine learning functions that the Scikit-learn library can handle are classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.
Theano is a python machine learning library that can act as an optimizing compiler for evaluating and manipulating mathematical expressions and matrix calculations. Built on NumPy, Theano exhibits a tight integration with NumPy and has a very similar interface. Theano can work on Graphics Processing Unit (GPU) and CPU.
Working on GPU architecture yields faster results. Theano can perform data-intensive computations up to 140x faster on GPU than on a CPU. Theano can automatically avoid errors and bugs when dealing with logarithmic and exponential functions. Theano has built-in tools for unit-testing and validation, thereby avoiding bugs and problems.
TensorFlow was developed for Google’s internal use by the Google Brain team. Its first release came in November 2015 under Apache License 2.0. TensorFlow is a popular computational framework for creating machine learning models. TensorFlow supports a variety of different toolkits for constructing models at varying levels of abstraction.
TensorFlow exposes a very stable Python and C++ APIs. It can expose, backward compatible APIs for other languages too, but they might be unstable. TensorFlow has a flexible architecture with which it can run on a variety of computational platforms CPUs, GPUs, and TPUs. TPU stands for Tensor processing unit, a hardware chip built around TensorFlow for machine learning and artificial intelligence.
Natural Language Processing
Keras has over 200,000 users as of November 2017. Keras is an open-source library used for neural networks and machine learning. Keras can run on top of TensorFlow, Theano, Microsoft Cognitive Toolkit, R, or PlaidML. Keras also can run efficiently on CPU and GPU.
Keras works with neural-network building blocks like layers, objectives, activation functions, and optimizers. Keras also have a bunch of features to work on images and text images that comes handy when writing Deep Neural Network code.
Apart from the standard neural network, Keras supports convolutional and recurrent neural networks.
PyTorch has a range of tools and libraries that support computer vision, machine learning, and natural language processing. The PyTorch library is open-source and is based on the Torch library. The most significant advantage of PyTorch library is it’s ease of learning and using.
PyTorch can smoothly integrate with the python data science stack, including NumPy. You will hardly make out a difference between NumPy and PyTorch. PyTorch also allows developers to perform computations on Tensors. PyTorch has a robust framework to build computational graphs on the go and even change them in runtime. Other advantages of PyTorch include multi GPU support, simplified preprocessors, and custom data loaders.
Pandas are turning up to be the most popular Python library that is used for data analysis with support for fast, flexible, and expressive data structures designed to work on both “relational” or “labeled” data. Pandas today is an inevitable library for solving practical, real-world data analysis in Python. Pandas is highly stable, providing highly optimized performance. The backend code is purely written in C or Python.
The two main types of data structures used by pandas are :
- Series (1-dimensional)
- DataFrame (2-dimensional)
These two put together can handle a vast majority of data requirements and use cases from most sectors like science, statistics, social, finance, and of course, analytics and other areas of engineering.
Pandas support and perform well with different kinds of data including the below :
- Tabular data with columns of heterogeneous data. For instance, consider the data coming from the SQL table or Excel spreadsheet.
- Ordered and unordered time series data. The frequency of time series need not be fixed, unlike other libraries and tools. Pandas is exceptionally robust in handling uneven time-series data
- Arbitrary matrix data with the homogeneous or heterogeneous type of data in the rows and columns
- Any other form of statistical or observational data sets. The data need not be labeled at all. Pandas data structure can process it even without labeling.
Matplotlib is a data visualization library that is used for 2D plotting to produce publication-quality image plots and figures in a variety of formats. The library helps to generate histograms, plots, error charts, scatter plots, bar charts with just a few lines of code.
It provides a MATLAB-like interface and is exceptionally user-friendly. It works by using standard GUI toolkits like GTK+, wxPython, Tkinter, or Qt to provide an object-oriented API that helps programmers to embed graphs and plots into their applications.
Python is the go-to language when it comes to data science and machine learning and there are multiple reasons to choose python for data science.
Python has an active community that most developers create libraries for their own purposes and later release it to the public for their benefit. Here are some of the common machine learning libraries used by Python developers. If you want to update your data science skills, check out IIIT-B’s PG Diploma in Data Science program.