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Why Python is Popular for Machine Learning Implementations

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This article covers the various machine learning (ML) implementation tools, the importance of Python for ML, and its characteristics. It also highlights the various open source ML packages in Python, providing a brief overview of some use cases. 

Machine learning (ML), which is a sub-field of artificial intelligence (AI), has been a hot topic in the recent past, disrupting various industries. ML is a method of understanding patterns in data and trying to make predictions, whereby computers automatically learn and improve from experience without being explicitly programmed. The goal of machine learning is to understand an existing process and try to predict the future by using a computer algorithm and/or statistical method.

Stanford University defines machine learning as “the science of getting computers to act without being explicitly programmed.”

Machine learning (ML) has opened the door to new concepts and technologies, new algorithms for robots, the Internet of Things, analytics tools, chatbots and more. Listed below are a few common ways the industry is currently using ML.

  • Analysing sales data: Improving and designing sales strategy
  • Real-time mobile personalisation: Promoting the experience
  • Fraud detection: Detecting pattern changes
  • Product recommendations: Customer personalisation
  • Learning management systems: Decision-making programs
  • Dynamic pricing: Flexible pricing based on a need or demand
  • Natural language processing (NLP): Understanding and conversing with humans

To achieve the above business models, different types of learning algorithms are used. The broad categories of algorithms that are used based on approach, inputs/outputs, the type of problem to be solved, etc, are listed below.

Supervised learning: This is a type of algorithm in which the model is first trained on input and the corresponding output data so that it can predict the output for a new set of inputs. Examples are regression analysis, classification, etc.

Unsupervised learning: When the system detects structures/patterns in data that may not be very apparent to us, it’s called unsupervised learning. Here the data is not labelled, but the system detects the groups by identifying the commonalities in the data. An example is finding groups or clusters.

Semi-supervised learning: This model falls between the supervised and unsupervised learning models. Unlabelled data, when used with a small amount of labelled data, is known to improve learning accuracy considerably.

Reinforcement learning: This is a training method based on rewarding desired behaviours and/or punishing undesired ones. As an example, a computer chess game keeps learning from us and surpasses our ability, learning new moves we have not yet used.

Industry adoption of Python in machine learning

The most common projects among ML implementers are information processing, NLP, planning and exploring, machine vision, and handling and control.

It is estimated that the global AI market will grow from US$ 7.27 billion in 2019, to reach over US$ 35.4 billion by 2025, and that AI and ML investments will continue to grow at a CAGR of 42.8 per cent, in the period 2019-2025.

Machine learning implementation software

A ML framework is an interface, library or tool, which allows developers to build ML models easily and quickly, without getting into the practical details of the underlying algorithms.

It provides a clear, concise way for defining ML models using a collection of pre-built, optimised components. This not only democratises the development of ML algorithms but also speeds up the process. Some of the key features of a good ML framework are:

  • Optimised for performance
  • Developer friendly; the framework uses traditional ways of building models
  • Is easy to understand and code on
  • Is not completely a black box
  • Provides parallelisation to distribute the computation process

Overall, an efficient ML framework reduces the complexity of machine learning, making it accessible to more developers.

Some of the most useful examples of software for ML are listed below.

  1. TensorFlow: Available in Python, C++, Haskell, Java, Go, Rust and JS languages
  2. Keras: A Python library for deep learning
  3. Microsoft Cognitive Toolkit: Open sourced, written in C++ and can be accessed from Python and C++
  4. Caffe: Python and C++ interfaces, written in C++

The languages popular for developing ML applications are: Python, Java, R and Scala.

Python: Python is the most used programming language for machine learning. Its popularity is due to the increased development of ML frameworks available for this language.  It is powerful for preprocessing data and for working with data directly.

The ML Python libraries are:

  • Scikit-learn: This is for data mining and analysis, which optimises Python’s ML usability.
  • NumPy: This is used for scientific calculations.
  • SciPy: This is used for advanced computation.
  • Pybrain: This is used for machine learning.
  • Pandas: This offers developers high-performance structures and data.
  • Java:  Java is widely used in enterprise programming. It is the second most preferred language used by data scientists and ML developers.

In terms of the ML applications in industry, Java tends to be used more than Python for network security, to control cyber-attacks and for fraud detection. The ML Java libraries are:

Deeplearning4j: This is an open source and distributed deep learning library.

MALLET: This allows for ML applications on text, including natural language processing, topic modelling, document classification and clustering.

Weka: This is a collection of ML algorithms to use for data mining tasks.

R: This open source programming language is used mostly for statistical computing. R is an easy-to-use programming language mainly used for data analysis, statistical computing and visualisation in ML. The most widely used packages for ML in R are:

  • Caret, which is for creating predictive models.
  • randomForest is for classification and regression.
  • e1071 includes functions for statistics and probability theory.

Scala: This language has its base in the Spark project, which supports ML through MLLIB. It has good libraries. Adoption has been low due to less community support and fewer users.

Why Python for machine learning

Python is widely being used in industry for ML applications. According to Web statistics, Python ranks second among the list of best programming languages. It is comparatively easier to pick up than other languages like Java, C++ and C, and is also acceptable from the performance point of view.

Python is a high-level, interpreted, interactive and object-oriented programming language. It was developed under an Open Source Initiative (OSI) approved open source licence. It is freely available, usable and distributable.

Python is designed to be easily readable. It has less syntactical construction than other programming languages, and is like the English language. The advantages of Python are:

  • It’s an easy and simple language to learn for beginners
  • Allows rapid prototyping
  • Syntax is easily readable
  • There is a vast variety and number of libraries available, both inbuilt and community created
  • Wide community support
  • Easy to integrate

Python supports developers during the entire software development lifecycle, and helps them to be productive as well as confident about the product they are building. It is used for a wide variety of applications including creating a functional website, easy data analysis, and better integration with any environment, as it is a platform-agnostic language.

Characteristics of Python

Open source Python has the following characteristics.

  • Interpreted: Processed at runtime by the interpreter. No need for compiling the program before executing it.
  • Interactive: Can run in an interactive mode, i.e., the program can be written in a command line shell, which gives an immediate output for each statement.
  • Cross-platform: Works on different platforms like Windows, Linux, Mac, etc.
  • Easy to learn and easy to use, apart from being developer friendly.
  • Expressive, easy to read and understand: The syntax of Python is like English statements.
  • Libraries: Provides a large set of libraries, modules and functions that enable the rapid development of applications.
  • Integrated: Easily integrated with other languages like Java, C/C++, etc.
  • Databases: Provides interfaces for a large set of databases.
  • Object-oriented: Supports the object-oriented style of programming that encapsulates code within the object.
  • Data types: Has a variety of basic data types like integers, floating point number, strings (both in ASCII and Unicode), lists, dictionaries, etc.
  • Modules and packages: Code can be grouped into packages and modules.
  • Memory management: Automatic memory management deallocates the memory instead of manually handling it in the code.

Web applications can be developed using Python. It provides libraries to handle protocols like HTML, XML, JSON, requests, etc. It provides various frameworks such as Django, Pyramid, etc, for Web application development.

Numeric computing applications: Provides various libraries and packages like SciPy, Pandas, IPython, etc, for developing numeric and scientific computations.

Key open source packages in Python for machine learning  

There are many open source packages in Python for ML problems. The ML solution is done based on the nature of the problem. Some of the most popular open source packages in Python for ML are listed below.

Numpy: This library provides support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

It handles linear algebra, Fourier transforms and random numbers. It interoperates with other libraries like Mat-plotlib, SciPy, Scikit-learn and TensorFlow. It is widely used in handling sound waves, images and other binary functions

Scikit-learn: This library provides a broad range of ML functions like classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.

The Scikit-learn developer team maintains a strong focus on code quality and comprehensive documentation with the help of the open source community. It can interoperate with numeric and scientific libraries of Python like NumPy and SciPy.

Theano: This scientific computing library allows defining, optimising as well as evaluating mathematical expressions, which deal with multi-dimensional arrays.  With Theano, making data-intensive calculations is up to a hundred times faster than when executing on a CPU alone.

Well optimised for GPUs and offering effective symbolic differentiation, Theano includes extensive code-testing capabilities. It can automatically avoid errors and bugs when dealing with logarithmic and exponential functions. Theano has built-in tools for unit testing and validation.

TensorFlow: This has an end-to-end library for performing high-end numerical computations. It can handle deep neural networks for image recognition, handwritten digit classification, recurrent neural networks, NLP, word embedding and PDE (partial differential equations).

It supports a variety of different toolkits for constructing models at varying levels of abstraction.  It has a flexible architecture with which it can run on a variety of computational platforms — CPUs, GPUs and TPUs.

Keras: This is used for constructing neural networks and for ML projects.  It interoperates with TensorFlow, Theano and R.  It runs efficiently on CPUs and GPUs.

Keras offers standalone modules including optimisers, neural layers, activation functions, initialisation schemes, cost functions, and regularisation schemes.  It functions as a user-friendly, extensible interface that enhances modularity and total expressiveness.

PyTorch: This supports computer vision, ML and natural language processing.  It integrates well with the Python data science stack, including NumPy.

It has a robust framework to build computational graphs on the go and even change them at runtime. It supports Tensor computation with the GPU, and helps with performance optimisation and scalable distributed training in research as well as production.

Pandas: This offers a wide range of tools for data manipulation and analysis. Using this library, we can read data from various data sources like CSV, SQL databases, JSON files and Excel. It can handle tabular data, time series data, matrix data, etc, and structure it into two types of data structures — data frames and series. Pandas is highly stable, providing an optimised performance.

Industry use cases of Python and ML

Healthcare: ML helps to monitor and predict diseases through applications, detects injuries, scans health parameters of patients regularly, etc. It can improve research, discover new drugs and help diagnose diseases more accurately. The ML technology has plenty of non-clinical uses also, such as the automation of administrative tasks and patient management systems.

Machine learning is also used for clinical trials of molecules, during epidemics like COVID-19, the discovery of new drugs, patient risk identification, etc.

Banking and finance: Fintech industries stand to gain much by using AI and ML applications because the latter can help in customising the user experience and detecting fraud — the two major concerns of this industry.

In addition, ML can be used in detecting customer attrition, loan defaulter prediction, fraud detection, credit scoring, money laundering prevention and portfolio management.

E-commerce: AI and ML based solutions help businesses to understand the shopper’s needs and create a better customer experience, thus increasing sales revenue. ML helps in content personalisation, chatbots for improving performance, dynamic pricing, identifying shoppers’ data patterns and predicting how responsive they might be to new prices.

Insurance: Python can help insurance companies in providing robust solutions for risk management, fraud management, personalised services, automation, customer support, etc.

Business services: Python is widely used for data gathering, predictions and ML tasks by business services.

Machine learning is used for chatbot creation in all the above sectors, personalising the customer experience and optimising labour usage.

The innovative services created with machine learning are already disrupting the markets. Python developers have kept pace by coming out with the latest libraries, frameworks and approaches to solving industry problems. These approaches enable businesses to use advanced ML techniques easily, to better their performance and improve the customer experience.



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