Why Machine Learning?
Machine Learning and Artificial Intelligence (AI) are now not only a reality but a key enabler to growth and productivity for businesses around the world. Once thought the realm of science-fiction and linked with stories of robots taking over Mankind, the truth about them today is much softer and more pleasant.
From Netflix’s recommendations to Google and Amazon’s voice assistant, our lives have enjoyed smart convenience from personal entertainment to business operations through the dynamism of machine learning. As more organisations sink this technology into their operations, the demand for developers can only grow. In fact, according to Harvard Business Review, 86% of companies surveyed say that AI is becoming a mainstream technology and many have plans to accelerate their AI adoption.
For most developers in this field, machine learning is synonymous with Python programming. Developed by the Dutch Guido van Rossum on 20 February, 1991, Python is well-loved by many giants in other industries, including those from Youtube, Industrial Light & Magic, and even Google.
Some key traits of Python
According to Python Institute, Python is “the language of today and tomorrow” because “Python is a widely-used, interpreted, object-oriented, and high-level programming language with dynamic semantics, used for general-purpose programming.” In fact, unbeknownst to many, Python is powering many well-used applications and devices.
Python’s ubiquitous success and adoption comes from it being:
- easy to learn
- is open-source and free
- an easy-to-read code
- allows for fast
- has extensive libraries
- is object-oriented
- fully compatible
- is scalable into a high
- level language
- has data structure built-in
Using a high-level language which is simple to pick-up but yet allows for complex design and applications is the main reason why Python is great for Machine Learning and AI. Here we provide a deep-dive of why Python is one of the best programming languages for Machine Learning and AI.
Python is simple and consistent
Python is a verbose programming language that reads somewhat like English. This makes Python easier to read and write. This trait is especially helpful when writing codes for something as complex as Machine Learning and AI and the verbosity of Python also invites efficiency when it comes to writing codes.
The intuitive nature of Python enables developers to focus on the logic instead of the syntax. This is why Python has risen to become the 3rd most popular technology.
“How will it play out in the future?”
Predictive analytics then attempts to play digital seer to address the next question in the equation. Will this trend continue or is it one-off? Will this trend grow or diminish? This third part of data analytics can forecast likelihood of recurrence, possible volume, or timelines.
At this point, even external research data can be included to bolster findings to assist with business strategies and outcomes.
If the coffee machine manufacturer realises their red machines are selling better in those two months, they can better manage stocks and store inventory in preparation for the increase in requests.
Python has an extensive selection of libraries and frameworks
Part of the reason why developers find it so easy and fast to build machine learning in Python is because of its extensive resources. Because Machine Learning and AI have such complex algorithms, tapping into these ready libraries and frameworks is like piggybacking upon others’ tried and tested formulations. Having these simplify the implementation of different common functionalities, and some of these helpful software libraries include:
- Keras, TensorFlow, and Scikit-learn for deep machine learning
- NumPy works with arrays and is for high-performance scientific computing and data analysis
- SciPy for advanced computing
- PyTorch to build computer vision and natural language processing applications
- Pandas for general-purpose data analysis
- Seaborn for attractive, high-quality data visualisation
Python is portable
What portability means essentially, is that it allows developers to move from machine to machine without any or much changes. The world’s three biggest platforms right now are Linux, Windows and macOS, and Python can be used on all of them with ease. This means that there’s less incompatibility issues and reduced resources needed to produce three versions of the same application – or to troubleshoot platform-specific bugs. The complexity of Machine Learning and AI means that this cross-compatibility gives valuable savings on time, costs and manpower.
Sometimes one will encounter projects coded in other languages. Python applications can be easily integrated with these other systems because it is both extensible and portable, allowing for cross-language tasks and for data scientists and developers to easily train machine learning models.
Network effect of Python
Whether you’re just investigating or already coding deep learning with Python, you’ll come to realise quickly that the Python community is huge because of how useful the language is. From Google using it to crawl web pages to Spotify recommendations, the pervasive use of the language across industries has fostered a giant following. The language’s open-source ethos and long history within developers also makes it that much easier to find support on forums across all platforms. Through the network effect, one can be certain that Python will only continue to grow in relevance and importance, and this also means that those skilled in Python will have a plethora of career opportunities
As the world accelerates its adoption of Machine Learning and AI, Python is poised to be a more mainstream and integral aspect in our daily lives.