Can I learn Machine Learning without Python?
Yes, you can learn machine learning and work on many huge machine learning projects without Python. There are many other programming languages you can use for machine learning such as Julia, R, MATLAB, Java, LISP, C#, C++, and many others.
It is worth noting that though you can learn machine learning without Python, Python still remains the most popular programming language for machine learning.
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 other languages. 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.
Although Python is very popular for machine learning as seen above, you can still learn machine learning with Python. You use other programming languages as discussed below.
WHAT ARE ALTERNATIVES TO PYTHON FOR MACHINE LEARNING?
There are many programming languages that you can use as an alternative to Python for machine learning. Here are some of the popular ones.
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.
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.
MATLAB is commonly used for machine learning to train models, tune parameters, and deploy to production or the edge. It is also used in deep learning for Data preparation, design, simulation, and deployment for deep neural networks.
MATLAB is heavily used in building machine learning models because of its flexibility. Another strength of MATLAB is that it can call functions and subroutines written in the programming languages such as C or Fortran to increase performance.
There are many companies that use MATLAB for machine learning, deep learning, automation, and many other tasks. Some of the popular companies include ABB, Mahindra, Broadcom, Aberdeen Assets Management, Intel, Microsoft, Cognizant, Bosch, Airbus, Land Rover, Qualcomm, HSBC, etc.
C++ is a very good programming language for machine learning. Although C++ is not easy to work with, there are still many engineers and scientists who use it for machine learning and artificial intelligence. C++ is a high-performant language and a lot of engineers use it for performance-critical models.
C++ machine learning libraries can be used for linear and nonlinear optimization, developing and training models, kernel-based learning algorithms, neural networks, classification, regression, forecasting, etc. They can also be used in deep learning for Data preparation, design, and simulation.
C++ has many popular machine learning libraries that help a lot of developers and engineers work on machine learning projects. Some of the popular C++ machine learning libraries include
- TensorFlow C++ API,
- Shark ML,
- Armadillo, and many others.
C++ is a common choice in machine learning when complex or custom functionality is required. Another strength of C++ is high performance; it is one of the few programming languages in which petaFLOPS computations have been achieved, others being C, Julia, and Fortran.
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 though Python is popular and widely used by many companies, engineers, and scientists for machine learning projects, you can still learn machine learning without Python. There are many other programming languages that you can use for machine learning.