The global machine learning industry had a market share of 88.71% in 2021. The tendency says, that further growth is inevitable. But why do we use ML in relation to artificial intelligence? The answer is simple. ML software is built to be used by AI applications. And, with the help of machine learning artificial intelligence can learn and improve its predictions, functioning, and user experience.
Both artificial intelligence and machine learning can advance the business processes of many companies these days. In 2021, 57% of customers marked that their user experience improved. More and more businesses started implementing AI and ML to get steps ahead of their competitors. And this worked!
However, it is not just about machine learning. As a technology, ML also uses different languages and tools to be productive. One of the languages that best suits machine learning is Python. Why is python good for machine learning? Let’s find out!
How does Python work?
You can trace back Python’s origin to the 1980s when Guido van Rossum started working on it. However, not until the 2000s that Python became what it is today. And it is an open-source programming language. Python is of the general-purpose, high-level, and outstanding code readability. Due to being object-oriented, it allows for writing clear and concise code used in small and enterprise projects.
How does python work?
Python is rather a dynamic language, an interpreted or, as it is known, bytecode-compiled one. According to Google Developer Education,
There are no type declarations of variables, parameters, functions, or methods in source code. This makes the code short and flexible. Python tracks the types of all values at runtime and flags code that does not make sense as it runs.
So, it seems, that Python has a lot of benefits among the other languages for machine learning. For example, Python’s
- easy to learn
- 100% compatible
- code is clear
- fast in development
- libraries are extensive
- open-source and free
- high-level language
- data structure is built-in
That’s why machine learning using python would only be an enormous advantage.
Machine Learning and AI: what's the difference?
Artificial intelligence is the simulation of human brain functionality in machines by programming them to think like humans and repeat human actions. For example, there are 4 types of AI as of today:
1. Reactive machines
Reactive machines react only to present scenarios. Unfortunately, they won’t rely on data that was taught to them or they recalled it. Such decision-making is impossible. Reactive machines work with maps or anything that requires planning ahead. Their observations focus on the live environment.
2. Theory of mind
In AI it is the equipment for computers, which can understand entities it interacts with a lot better. These might be autonomous cars that are able to predict the behaviors of both pedestrians and drivers.
3. Limited memory
It is the type of AI learning from past experiences and building knowledge on past observations. Apart from the past information obtained (historical, observational), limited memory uses also pre-programmed data to predict and classify (especially, if the task is complex). For example, let’s take self-driving cars. These cars store the recent speed, the distance, the speed limit, and other information about cars on the road to be able to navigate correctly.
Self-aware machines are robots that can perceive their internal states and the emotions, and behaviors of others. This type of AI is still to be developed and advances. Of course, you can hear about intelligent responsive robots, but they still lack human skills.
Artificial intelligence is also divided into subsets. Here is where it includes machine learning, big data, and natural language processing. That’s where you know about Face ID on smartphones, search algorithms (i.e. recruiting and others), and algorithms giving out recommendations.
Machine learning, in its turn, is the type of artificial intelligence. It makes software apps more accurate at predicting outcomes. What’s more, to predict even without being programmed beforehand. Machine learning is based on modeling and algorithms that require historical data to predict something new.
And that’s the difference between AI and ML. Artificial intelligence uses machine learning to be able to learn, and predict future events.
4 reasons why Python is the best language for Machine Learning
As you have found out about artificial intelligence and machine learning, let’s get back to why is python used for machine learning. And, there are at least 4 good reasons to use python for machine learning. Let’s enumerate:
1. Simplicity and consistency
AI algorithms and machine learning models are complex predictive technologies that Python can simplify. How? With its clear code, lots of machine learning-specific libraries, possibility to shift focus from the language towards algorithms. Also, it is quite easy to learn, consistent, and intuitive. That’s why Python receives 3rd place as the most popular technology. 48.24% of developers gave their votes for this language.
2. Variety of libraries and frameworks
There is a vast database of libraries and frameworks that Python uses for machine learning purposes. For example,
- NumPy works with arrays, in some parts of linear algebra, and different matrices.
- Keras, which is a deep learning API running on Tensorflow to make it possible to experiment fast.
- Tensorflow - a free open source library for both ML and AI that focuses on training and deep neural networks.
- Matplotlib is a library that allows the creation of visualizations (static, animated, interactive) in Python.
- Seaborn - a data visualization library based on Python, which gives an opportunity to draw graphics (statistics), which are attractive and of high quality.
- PyTorch is an open-source ML library used to build computer vision and natural language processing applications.
3. Platform independence
Software solutions developed with Python can be built and also can run on multiple operating system platforms. For instance, Linux, Windows, Mac, Solaris, and more. This makes python programming machine learning a lot more convenient. That’s why developers enjoy Python in the process of developing ML apps.
4. Great community
These are the most prominent benefits of python for machine learning. But there is even more to observe.
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