If you’ve taken Andrew Ng’s course on machine learning, you learned it using Octave/MATLAB. However, when you do a Google search, almost nobody seems to be talking about Octave as a programming language for machine learning. As a beginner, it’s natural to wonder why Octave has little support online even after being taught in the go-to introductory course for machine learning and is it even good?
Octave is good for machine learning in terms of helping you better grasp the mathematical intuition behind the algorithms. However, it’s not the best language for implementing ML or using it in production. Python has much better support for machine learning than Octave.
This article will discuss what Octave is and how you can use it to learn machine learning. We’ll also compare it with other programming languages to determine which is the best language for machine learning.
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What Is Octave?
Octave is a high-level programming language that’s designed for numerical computations. You can quickly solve linear and non-linear numerical problems and perform other experiments numerically. This language first originated in 1988, and the first alpha version was released in 1993.
Octave is quite similar and mostly compatible with MATLAB. If a code runs on MATLAB without using any functions that Octave doesn’t have, it will also run on Octave. It even has several language features and syntax variety that MATLAB lacks.
Although Octave was initially meant for scientific computation, many organizations use it for basic data processing and plotting. It can also be used for machine learning, analyzing data, and building ML algorithms.
Why Was Octave Used in Andrew Ng’s Machine Learning Course?
Andrew Ng has a classic free course on machine learning that’s available on Coursera. In the course, Andrew starts from scratch and goes very deep into the behind-the-scenes of machine learning algorithms. It’s a very popular and highly rated machine learning class.
Chances are, you heard about Octave and MATLAB from this course. Andrew Ng has explained that these languages have a syntax similar to linear algebra notations. So they are less confusing than other languages that don’t follow the same convention.
The goal of Andrew Ng’s course is to build a mathematical foundation for your ML journey. Octave helps you understand the mathematical essence of ML problems. Another reason for using Octave is that this course was created before Python became the go-to language for machine learning.
However, it should be noted that the course we’re talking about is quite old, though it is still relevant and recommended by many. Andrew Ng’s latest machine learning classes have switched to Python.
In fact, students have even converted all assignments of this course from Octave to Python. This means you don’t have to learn Octave to benefit from this incredible ML course. Check these converted assignments out here.
Is Octave a Good Language for Practical Machine Learning?
We’ve discussed that Octave can allow you to understand the mathematics behind machine learning algorithms better. However, Octave is not a good programming language for machine learning in a production environment.
There are a couple of reasons why Octave is not suitable for ML. First, it doesn’t have the same functionality as other languages used for ML. Second, there’s not much support for Octave if you get stuck and need help with something. Octave also doesn’t have libraries and frameworks to speed up ML tasks. It’s not as flexible, simple, and feature-rich as other programming languages.
The bottom line is that almost no one uses Octave for machine learning, and you shouldn’t either.
Which Is the Best Language for Machine Learning, if Not Octave?
So now you know that Octave is not very suitable for building practical machine learning projects. And you’re probably wondering which language you should learn instead. Well, Python is the most widely used programming language for machine learning. Experts consider it the best language for ML and AI.
Python offers simple, concise, and readable code. Its simplicity allows you to write robust and reliable programs. It also lets you focus on solving the ML problem instead of getting lost in the language’s technical nuances. Python is also highly intuitive, making it easy to learn for beginners.
Python has extensive libraries for ML. It has a library for virtually everything related to ML. Scikit-learn, Pandas, TensorFlow, Keras, etc., have become standard libraries for various ML tasks. You can also choose to code in your style with Python, whether it is using OOPs or scripting.
Lastly, Python has been around for decades. It has developed a large and helpful community. Extensive documentation is available online. You can find thousands of question-answers and community guides for different functionalities of the language. The large user community is also because big companies like Facebook and Google use Python in their systems.
Are There Any Alternatives?
Python is the recommended programming language for machine learning, but there are also a few alternatives. These languages may not be as popular, but they have their advantages. Let’s look at a few alternative programming languages for machine learning other than Python and Octave:
After Python, R is the recommended ML programming language. R is excellent for data visualization and statistics. It is often the preferred language for applications with a lot of statistical data. Like Python, R is a flexible language offering cross-platform compatibility. It also has a growing, helpful community to help you if you get stuck.
However, R is often reported to be laggier and slower as compared to Python when dealing with large-scale data products.
Java is quickly gaining popularity among ML enthusiasts who come from a Java background. Using Java for machine learning removes the burden of learning another programming language like Python or R. Many companies already have big Java codebases, and you’ll find that they explicitly mention Java as a requirement.
Like Python and R, Java also has several libraries for machine learning. Several popular data-processing tools like Hadoop, Spark, and Hive are written in Java. As a machine learning engineer, you’re likely to encounter Java or Java-based programs in your career.
Julia is a general-purpose programming language with many features exclusive to machine learning. It is a high-performance language, providing excellent speed without any handcrafted profiling or optimization techniques. Big corporations like Apple, Oracle, Disney, and NASA use Julia in their systems.
Another great thing about Julia is that its code is universally executable. This means you can write a piece of code in this language and use it in languages like R or Python in a wrapper like RCall or PyCall.
Lisp, founded in 1958, is the second oldest programming language. It’s a dynamically typed language that has influenced several other programming languages like Java, Julia, and Python. Even though it’s such an old language, developers still use Lisp for machine learning and artificial intelligence projects.
The thing about Lisp is that it lacks the support of popular ML libraries. It’s also not a beginner-friendly language, nor does it have a large user community like R or Python.
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Octave is a high-level programming language that is typically used for scientific numerical calculations. Andrew Ng has used Octave and MATLAB in his course on machine learning. The reason is that these languages allow you to better understand the mathematics behind machine learning algorithms. They make things intuitive for beginners.
However, Octave is not the best programming language for practical machine learning. It doesn’t have the same level of support or number of libraries to help ML engineers build ML algorithms. Instead of Octave, Python is the preferred programming language for machine learning, followed by R, Java, Julia, and Lisp.
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