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Can PHP be used for Machine Learning?
Yes, PHP can be used for machine learning using libraries like Rubix ML. but PHP is not a good option for Machine Learning, there are many other programming languages that you can use such as Python, Matlab, Julia, R, C++, Go, Java, and many others.
Since its invention in 1994 by Rasmus Lerdorf, PHP has grown in popularity and usage to become one of the most widely used programming languages. PHP is more suitable for web development, it powers more websites on the internet than the rest of the server-side programming languages put together.
But when it comes to Machine Learning and Artificial Intelligence, PHP is not a popular choice. This is reflected in the scarcity of PHP-based machine learning and artificial intelligence libraries.
PHP is used by many popular companies such as Wikipedia, Facebook, Slack, Etsy, Tumblr, MailChimp, Flickr, Moodle, Trivago, Delivery Hero, Lyft, and many others. But nearly none of these companies use PHP for machine Learning.
If you want to use PHP for machine learning and artificial intelligence, the major library to pick is Rubix ML. Rubix ML is an open-source high-level machine learning and deep learning library for PHP. It has over 40 supervised and unsupervised learning algorithms and it Supports pre-processing and cross-validation
Rubix has been for many machine learning projects such as Unsupervised Color Clustering, Human Activity Recognizer, Credit Risk, Predict House Prices, Sentiment Analysis from Movie Reviews, etc. Other PHP-based Machine Learning Artificial Intelligence libraries include PHP-ML and Brainy.
Seeing that PHP is not popular and widely used in machine learning and artificial intelligence, it is better to choose a programming language that is better suited for machine learning. PHP is useful for machine learning for small data sets, but you will need other languages to work on huge machine learning projects.
WHAT ARE ALTERNATIVES TO PHP FOR MACHINE LEARNING?
There are many programming languages that you can use as an alternative to PHP for machine learning. Here are some of the popular ones.
Python has an easy to understand syntax that makes it easy to write code. Getting started with Python for machine learning is much easier than with PHP. Python is used a lot by developers for machine learning and artificial intelligence.
According to a 2022 Stack Overflow survey, Python is the third most used programming language by professional developers. It is also one of the most loved programming languages with 67.34% of developers who loved it versus 32.66% of developers who dreaded it.
Python has a lot of resources and libraries that you can use for machine learning and artificial intelligence. Here are some of the popular ones.
TENSORFLOW – An open-source library for developing and training machine learning models. Tensorflow also helps developers to build and deploy machine learning-powered applications. It is used by many companies such as PayPal, Bloomberg, eBay, Dropbox, IBM, Coca-Cola, Google, Airbnb, DeepMind, Uber, Snapchat, Qualcomm, Airbus, Intel, Twitter, and many others.
KERAS – an open-source, high-level, deep learning API developed by Google for implementing artificial neural networks. It can run on Tensorflow, Theano, Microsoft Cognitive Toolkit, and many other platforms.
NUMPY – Numpy is the most popular package for scientific computing with Python. It is used for machine learning, data science, visualization, Array libraries, image processing, signal processing, etc. Numpy also powers many other scientific and machine learning libraries.
Other Python-based machine learning libraries include SCIPY, SCIKIT-LEARN, PYTORCH, PANDAS, THEANO, MATPLOTLIB, NLTK
Julia is a great language for machine learning, it is a high-level, high-performance dynamic language. Julia is used for Machine Learning, Scientific Computing, Parallel Computing, Data Science, Data Visualization, etc.
Julia has a lot of packages for machine learning, some of the popular ones include MLJ.jl, Flux.jl, Knet.jl, AlphaZero.jl, Turing.jl Metalhead, ObjectDetector, and TextAnalysis.jl. These packages will helpful for Deep Learning, decision trees, clustering, pre-trained models, reinforcement learning algorithms, etc.
For example, the Federal Reserve Bank of New York used Julia to make models of the United States economy (including estimating COVID-19 shocks in 2021), noting that the language made model estimation “about 10 times faster” than its previous MATLAB implementation.
Julia is a very fast and high-performance language compared to many programming languages used in machine learning. Julia is one of the few high-level programming languages in which petaFLOPS computations have been achieved, others being C, C++, and Fortran.
Julia is the 5th most loved programming language, it is used by many companies for machine learning, these companies include Aviva, NASA, Brazilian INPE, Moderna, BlackRock, Climate Modelling Alliance, Google, Microsoft, and many others.
Matlab is one of the most popular numerical computing platforms among engineers and scientists. It is used for Machine Learning, Deep Learning, Algorithm development, Image processing & Computer Vision, Automated Driving Systems, Data Science, and more.
In machine learning, you can use MATLAB to train models, tune parameters, deployment of deep neural networks, and deploy to production or the edge. MATLAB is a great choice for Machine Learning.
It is reported that MATLAB has more than 4 million users. Some of the companies using MATLAB include AMD, DoubleSlash, Broadcom, General Electric, Volvo, Zendesk, Confidential Records Inc, Lucid Motors, Blue Origin, Lockheed Martin, and many others.
R is an open-source software environment for statistical computing and graphics. R is heavily used in building machine learning models because of its flexibility. There are many R packages that you can use for machine learning.
Some of the popular ones include Classification And Regression Training (CARET), Data Explorer, Dplyr, Ggplot2, mlr3, Xgboost, Superml randomForest, e1071, and many others. Companies using R include Amazon, Google, Flipkart, Firefox, LinkedIn, ANZ, Accenture, Infosys, etc.
C++ is another great and popular option for machine learning and artificial intelligence. You can use libraries such as Caffe, Microsoft Cognitive Toolkit, MLPack, Shark, Gesture Recognition Toolkit (GRT), and many others.
These libraries are helpful for deep learning, artificial neural networks, classification, regression, forecasting, linear and non-linear optimization, algorithm development, and more.
Java has a lot of libraries for machine learning. Some of the popular libraries include Weka, Apache Mahout, Deeplearning4j, Mallet, Spark MLlib, JSAT, Encog Machine Learning Framework, JavaML, Massive Online Analysis (MOA), and many others.
These libraries are helpful for deep learning, classification, artificial neural networks, regression, forecasting, clustering, association rules, recommendation, and more.
It can be seen that even though PHP can be used for machine learning and artificial intelligence, it is not better suited for the task. There are very few PHP-based machine learning libraries and resources to work with.
In order to effectively work on machine learning projects, programming languages such as Python, Julia, R, MATLAB, C++, Java, or C# are preferred over PHP.