Machine Learning — a term you’ve probably heard of unless you were living under a rock since the past 2–3 years. But what exactly is it? To put it in simple terms, whenever you shop from amazon for a mobile phone, it suggests you a case or a screen protector for that mobile phone. Or when you buy a t-shirt online it also suggests you jeans and shoes. It predicts your future by analysing your browsing history and time spent on a single product before buying it. There are 3 types of learning algorithms which make it so powerful. Supervised, Unsupervised, and Semi-Supervised Learning. We won’t dive into the specifics of it but basically supervised learning is the data which you give to differentiate between two objects while unsupervised learning is data that hasn’t been labelled by the user. Semi-Supervised learning, on the other hand, is the data which is half-fed for example a large amount with a small amount of labelled data.
Machine Learning has become extremely important in almost every spheres. So much so that it can predict, with certain assurity, when are you likely to have a heart attack based on your electrocardiogram tests. Fascinating, right? Tech CEOs have praised Machine Learning and almost bet that it’s going to be a big deal in the next 10–20 years. Sundar Pichai, the chief executive officer of Alphabet’s Google said he sees a “huge opportunity” in ML. While Jeff Bezos, the richest man on the planet and also CEO of Amazon said, “It’s really early but I think we’re on the edge of a golden era. It’s going to be so exciting to see what happens.”
You can see Machine Learning at work in almost all parts of your life, for example: Google Assistant- the voice recognition assistant uses Machine Learning to differentiate between voices and accents. Amazon Alexa, Amazon dot com use Machine Learning to improve your experience throughout the amazon ecosystem. If you have Face ID on your iPhone X or later, it uses Machine Learning to calculate the number of dots on your face and unlock the phone- all under the span of 10 milliseconds. Cool, huh? It uses what is called Deep Neural Networks to create a mathematical model of your face.It is expected to solve problems that were merely a science fiction back then.
Using Python for Machine Learning
Python is mainly a scripting language that is very easy to learn compared to other programming languages like C++ and JavaScript. It doesn’t require a lot of programming expertise, and if you’re able to think logically, half your work is done — because Python doesn’t rely a lot on syntax.
Python has a larger user base compared to many other programming languages which means more people would be able to understand and participate in your work which makes it easier to work on new technologies from scratch. Developers can find tutorials and tips in the development process. Unlike other languages, Python maintains clean and concise code throughout the project which helps in writing complex algorithms and maintaining them. Python user community has developed many modules to help programmers implement machine learning such as SciKit and Theano. These modules are well-documented to help you step-by-step on your first project. The development is fast and stable which speeds up the process by a lot when fixing bugs.
Advantages of using Python
- Ease of Use- Python is very easy to use and developers can quickly start adapting to the language.
- Speed- There is less work required in a code as compared to other languages which saves time.
- Reliability- The Modules are well documented which makes the experience a bit easier.
- User base- Thanks to the huge user base Python has to offer, more people can get involved in your project and help iron out the bugs.
- Huge Library- Already built libraries such as SciKit and Theano makes for a good start for a beginner.
- Integration through all operating systems- It can run on all modern operating systems through the same code. So for example you could build a project in MacOS, Test it in Linux and upload it through Windows.
Conclusion
In the end, it’s all about personal preference, some people prefer other programming languages for their projects, some people prefer Python. C++/Java can also be used for Machine Learning but it’s the simplicity of Python that makes it the most popular language for Machine Learning.The integration, huge number of libraries and an active community are a huge plus if you’re a beginner trying to get into Machine Learning, Python is a great programming language to kick start and explore the vast capabilities Machine Learning has to offer. It is an extremely accessible language to learn and begin scripting in, important for people coming from non-software backgrounds.
Now that you’re well versed with the benefits that Python offers, it’s time you get yourself started with it. Why not come by and check out the Python courses offered by Coding Ninjas? We also offer courses on Machine Learning to ensure you’re on the correct path right from the start.
To read more about Python frameworks, click here.
