Artificial intelligence (AI) and machine learning (ML) continue to become more popular, and you'll find the technology in everything from smartphone apps and computer programs to smart technology and appliances – and automobiles (think self-driving cars). These technologies are no longer confined to scientific computing and statistical research, but have become, for the most part, part of everyday life.
AI aims to make computers self-aware and self-sufficient by simulating human intelligence. ML is a subset of artificial intelligence that allows computers to learn and improve based on experience. It determines your program based on experience from past inputs and outputs and is currently the most widely used application of AI.
What, in part, allowed machine learning to develop so quickly were initiatives like uTensor and TensorFlow. uTensor is a free, open-source, integrated ML infrastructure designed for rapid prototyping. TensorFlow Lite is Google's ML framework that enables the deployment of machine learning models across devices.
It's perhaps no surprise that computer engineers, developers, and programmers are highly sought after and represent some of the fastest-growing occupations – with a particular emphasis on AI and ML expertise.
The first step to becoming an expert in computer programming is to first learn and choose the ideal programming language. What makes this challenging is that there is no single language. ML and deep learning models can be implemented in many programming languages. And each has its own set of tools, libraries, and packages that implement various ML models.
Just as ML is a subset of AI, deep learning is a subfield of machine learning that relates to algorithms and “teaching” for example. For example, deep learning is a key technology behind driverless cars, allowing them to recognize a stop sign or a pedestrian.
The choice of programming language selected for such projects largely depends on its purpose and industry. For example, in web application development, JavaScript is the most used programming language due to the MEAN and MERN stacks. Therefore, if a developer is implementing an ML model for a web application, JavaScript is the obvious choice.
Similarly, if ML is required for a desktop game, C++ becomes the best choice. However, it should be noted that no programming language is confined to any one industry or software solution. There are certainly web applications developed in Python, Java, PHP and .NET. But a developer will likely use Java to implement ML models if the solution they are working on is in Java (Java is different from JavaScript, as you will learn below).
For starters, Python has become the most popular programming language for deep and machine learning. Python is easy, versatile and has a large number of libraries and frameworks that allow you to implement machine and deep learning. In fact, the simplicity with which these applications can be developed in Python has made it the third most preferred coding language in the world.
The language used will depend on the programmer, the project and the industry. Let's review the different programming languages used with machine learning models, including the pros and cons of each, and explore their tools and frameworks.
Phyton
Python started out as a high-level programming language with a simple syntax. It is widely used for web development, data analysis, scripting, machine learning, and desktop development – and for good reason.
Currently, no other programming language is as accurate or easy to use, providing high code readability, which means that its users do not need to be computer engineers or program developers. Python is considered to have zero learning curve and a developer can start using its specific ML tools and frameworks without training.
Code readability is appreciated as the math and statistics behind ML algorithms can often be complicated.
This feature also means that a large team of developers can focus on program development without getting involved in language-specific complexities. Python is highly flexible and efficient.
Part of its effectiveness is related to the many libraries, tools and frameworks available that are mature enough to handle the diversity or challenges of any AI problem. A developer would rarely need to write a library or module from scratch to implement an ML model in Python. It already has the tools for almost all machine learning tasks.
These features make Python seem like an ideal choice for all projects, but it depends on the project. Python is not suitable for low-level programming. For example, it is not possible to write hardware-level applications using it. And despite a simple syntax, Python is not ideal for most front-end development. Even in the web domain, Python is not considered suitable for single-page applications.
Instead, Python is the best choice when implemented on the backend or when ML is implemented on embedded computers such as single-board microcomputers and AI development boards.
Some of the notable Python tools, libraries, and frameworks used in ML include:
-NumPy : NumPy or Numeric Python is a library used for linear algebra, Fourier transform, matrix calculation and multidimensional matrices. Essentially, ML involves manipulating data sets. NumPy is good at managing memory usage for arrays and matrices.
– Pandas : a library for data analysis, data wrangling, and data visualization. It can handle large datasets including real-time streams.
–Matplotlib : A library used for data visualization. It is useful for creating tables, graphs, histograms, and other graphical representations of data.
– Born in the Sea : a library for data visualization. It is built on top of Matplotlib and provides several built-in plots that are useful for graphical representation of complicated data sets.
-SciPy : A library built on top of Numeric Python and useful in higher-level visualization and graphical data manipulation.
– Sci-Aprenda Kit : a tool used for ML modeling. It has several features for implementing an ML model via data mining and analysis.
–Keras : a tool used to create deep learning models. It supports multiple backend neural computing engines and is widely used for distributed training of deep learning models.
– TensorFlow : an open source library used to create large-scale deep learning models.
– PyTorch : is an optimized tensor library used for deep learning using GPUs and CPUs.
– OpenCV : an open source library used for image processing and computer vision.
– Science Kit Image : an open source library used for various image processing tasks.
– NLTK : a library used for natural language processing in Python.
–Librosa : a library for audio and music analysis.
- For preparing datasets and manipulating data, NumPy and Pandas are used.
- For data visualization, Matplotlib, Seaborn and SciKit Learn are used.
- For ML modeling, Sci-Kit Learn is used.
- For text analysis, NLTK, NumPy and Sci-Kit Learn are used.
- For image segmentation and other image-related ML tasks, Sci-Kit Image and OpenCV are used.
- For audio analysis and associated ML tasks, Librosa is used.
- For deep learning, TensorFlow, Keras and PyTorch are used.
- For scientific computing, Sci-Py is used.
Python is mainly preferred for web mining, image segmentation, natural language processing and chatbot development.
Java
Java is a high-level programming language and platform that was released in the mid-90s and is considered the jack of all trades. It has been widely applied in smaller applications and large enterprise developments. Many companies have their infrastructure, codebase, and applications written in Java.
One benefit is that code written in Java can be ported to any platform. It also has several libraries for manipulating datasets, organizing data, exporting and importing, visualizing and analyzing data.
There are several frameworks for big data analysis written in Java, including Hadoop, Spark, Hive, and Fink. It tends to be the preferred choice for ML when the existing code base is already written in Java or when big data analysis is involved. Developers typically prefer Java to implement ML algorithms rather than migrating other languages such as Python or R.
Some of the notable Java tools, libraries, and frameworks used in ML include:
–Weka : a collection of ML algorithms used for data analysis, data mining and predictive analysis. It can be easily used for general-purpose ML tasks with graphical interfaces.
–JavaML : A collection of ML algorithms for general-purpose machine learning tasks.
–Apache Mahout : A scalable ML library that is useful for data mining in distributed architectures.
–Apache Spark : a Java platform for big data analytics on top of Hadoop.
–DeepLearning4j : A Java library useful for deep learning on single machine and distributed architectures.
– HAMMER : useful for natural language processing in Java.
– Massive Online Analytics : An open source software for mining data from real-time streams.
–ELKI : a Java framework mainly used for unsupervised learning. Machine learning in Java is commonly used for customer support services, cybersecurity, and fraud detection.
R
R is a programming language for high-level statistical computing, particularly large numerical and graphical data sets. It focuses on the mathematical calculations behind statistical and machine learning algorithms and is supported by the R Core Team and the R Foundation for Statistical Computing.
R is considered superior to Python in terms of data visualization and analysis. It is also open source, free to download and offers more than 12 thousand packages/libraries in the CRAN repository. This makes it a cost-effective alternative to similar solutions such as MATLAB or SAS.
It is cross-platform and offers a wide variety of packages for almost every ML task imaginable. It is also flexible for use with other tools and frameworks.
However, R is not an easy-to-use language for machine learning. Most R packages are third-party contributions and lack complete documentation, so users need experience as it can be challenging to learn, write, and maintain production code.
RStudio, which uses the R language to develop statistical programs, provides a complete integrated development environment for data visualization and ML application development.
Some of the notable Java tools, libraries, and frameworks used in ML include:
– Tidyr : used for organizing, cleaning and organizing data.
– Ggplot2 : used for data visualization.
–Dplyr : Used to manipulate data, export and import data to external databases, and data organization.
– Tidyquant : used for commercial and financial analysis.
– RATS : used to handle missing data values.
– PARTY : is used to create data partitions.
– part : a tool for creating data partitions.
– Rmarkdown : Used to report insights from machine learning models.
– CARET : Used for supervised machine learning such as classification and regression.
R is mainly used for statistics-heavy applications such as bioengineering, biomedical statistics, financial analysis, fraud detection, and sentiment analysis.
C++
C++ is a cross-platform language used to create high-performance applications and was created as an extension of the C language. C++ gives programmers a high level of control over system resources and memory, but it is not the easiest language to use for ML.
Compared to Python, however, C++ has benefits. It can be used to write hardware-level programs, allowing the developer to exercise strict control over memory and CPU usage.
C++ is preferred where the execution speed of the ML algorithm is extremely significant, such as for developing ML models for the Internet of Things.
Some of the notable C++ tools and frameworks used in ML include:
– mlpack : used for general purpose ML. It is highly scalable and easy to use.
– Shogun : A collection of tools used for data visualization, classification, and many other machine learning tasks.
– TensorFlow : used for deep learning using multilayer neural networks.
– coffee : a deep learning framework that offers great execution speed and scalability.
– Torch : a deep learning framework particularly useful in scientific computing and numerical analysis.
– Microsoft Cognitive Toolkit : A C++ deep learning framework for creating ANN.
–DyNet : a deep learning framework for natural language processing, unsupervised learning, and reinforcement learning.
C++ is mainly used to implement ML in game development, cybersecurity, embedded systems, and robotics.
JavaScript
While Java is a cross-platform object-oriented programming language, JavaScript is a cross-platform scripting language designed to help develop interactive web pages. It follows the rules of client-side programming, running in the user's browser but without the need for any web server resources. JavaScript is commonly used for full-stack development on the MEAN (MongoDB Express Angular Node.JS) and MERN (MongoDB Express React Node.JS) technology stacks.
JavaScript can be used for ML development on the front-end (within the browser) and back-end (within Node.JS). Machine learning in JavaScript is mainly attracting
Developers using JavaScript must implement machine learning on the front end, running pure HTML instead of using back-end servers.
Some of the notable JavaScript tools, libraries, and frameworks used in ML include:
– Tensorflow.js : A popular ML library in JavaScript. It can be used to create almost any ML model using web APIs.
– Math.js : a useful library for working with numbers and mathematical analysis.
– machinelearn.js : equivalent to Python’s Sci-Kit Learn. It is useful for supervised and unsupervised learning in JavaScript.
– Brain.js : Used for GPU-accelerated deep learning in the Node.JS backend.
– OpenCV.js : a useful library for image processing in JavaScript.
– face-api.js : an API for facial detection and recognition. It can be easily integrated into the front-end and Node.JS.
JavaScript-based ML is mainly used in online gaming, network monitoring, content recommendation engines, image classification, and object detection.
scale
Scala is a statically typed programming language. It runs on the Java platform (Java virtual machine) and is compatible with Java libraries and programs. Essentially, it was designed by simplifying Java for machine learning.
Because it is a compatible language (that is, its translators generate machine code from the source code), it is faster than Python for code executions. Scala is ideal for ML on large databases where big data analysis is involved, such as Apache Spark.
As it uses functional and object-oriented programming, it is quite difficult to learn. It is often chosen by Java developers looking for big data analysis in distributed frameworks.
Some of the notable Scala tools and frameworks used in ML are:
– Selim : Useful for data analysis and general-purpose machine learning.
– Brisa : a Scala library for scientific computing.
– Aerosol : useful in implementing GPU and CPU accelerated ML.
– Scalalab : useful for Matlab-like functionality.
– NLP : used for natural language processing in Java.
Scala is mainly used to work with big data. It is useful in parallel computing and DSL (Domain Specific Language) computing.
Julia
Julia is a general-purpose dynamic programming language typically used for deep learning, suitable for numerical analysis and computer science.
Its syntax is similar to a scripting language like Python and R. It also offers excellent execution speed and parallel computing similar to Java and Matlab.
Julia is mainly preferred for backend deep learning and used along with Python for frontend deep learning. Although it can also be easily used with C and Python tools and libraries.
Some of the notable Julia tools and frameworks used in ML are:
MLBase.jl : A Julia package used for general-purpose machine learning tasks such as data manipulation, testing and validation, model tuning, and model performance evaluation.
– Flow : A lightweight package that covers similar functionality to TensorFlow.
– TensorFlow.jl : a TensorFlow-like package written in Julia.
–SciKitLearn.jl : a package that covers similar functionality to Sci-Kit Learn. It is mainly used for supervised learning.
– Knet : A GPU-accelerated deep learning framework in Julia.
Julia is mainly used in scientific computing and parallel programming.
Final thoughts
Machine learning models and deep learning networks can be implemented in a variety of coding languages. Developers and programmers make their choices based on the infrastructure, the existing code base, and their own experience with a specific coding language.
For beginners, Python is the easiest language to start exploring the field of AI and ML. For those who want to use machine or deep learning in embedded systems and the Internet of Things, Python is once again the ideal choice.
For those interested in high-performance ML in robotic locomotion and embedded systems, C++ may be the preferred choice. Ultimately it's up to the programmer.
