Original article was published by Ajay Kapoor on Artificial Intelligence on Medium
Why do we use Python for Machine Learning?
You might have heard these words together: AI, Machine Learning, and Python. The reason behind this is that Python is one of the most suitable languages for AI and ML. Python is one of the simplest programming languages and AI and ML are the most complex technologies. This opposite combination makes them be together.
In simple words, I would like to make this clear that Machine learning is really complex technology. Its algorithms are really complex and difficult to understand. And on the other hand, Python is considered to be one of the simplest languages. Its syntax structure and coding length are really short to understand. That is why Python is considered to be the most suitable language as it can manage complex algorithms in the simplest way.
Let’s discuss these terms individually with their quick definition:
What is Artificial Intelligence?
According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”. Artificial Intelligence is a way of making a computer, a computer-controlled robot, or software that thinks intelligently, in a similar manner the intelligent humans think.
AI is accomplished by studying how the human brain thinks and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems.
The Necessity of Learning AI
As we know that AI pursues creating the machines as intelligent as human beings. There
There are numerous reasons for us to study AI. The reasons are as follows:
AI can learn through data
In our daily life, we deal with huge amounts of data and the human brain cannot keep track of so much data. That is why we need to automate things. For automation, we need to study AI because it can learn from data and can do repetitive tasks with accuracy and without tiredness.
AI can teach itself
It is very necessary that a system should teach itself because the data itself keeps changing and the knowledge which is derived from such data must be updated constantly. We can use AI to fulfill this purpose because an AI-enabled system can teach itself.
AI can respond in real-time
Artificial intelligence with the help of neural networks can analyze the data more deeply. Due to this capability, AI can think and respond to situations that are based on the conditions in real-time.
AI and Python: Why?
- The obvious question that we need to encounter at this point is why we should choose Python for AI over others.
- Python offers the least code among others and is in fact 1/5 the number compared to other OOP languages. No wonder it is one of the most popular in the market today.
- Python has Prebuilt Libraries like Numpy for scientific computation, Scipy for advanced computing, and Pybrain for machine learning (Python Machine Learning) making it one of the best languages For AI.
- Python developers around the world provide comprehensive support and assistance via forums and tutorials making the job of the coder easier than any other popular language.
- Python is platform Independent and is hence one of the most flexible and popular choiceS for use across different platforms and technologies with the least tweaks in basic coding.
- Python is the most flexible of all others with options to choose between OOPs approach and scripting. You can also use IDE itself to check for most codes and is a boon for developers struggling with different algorithms.
Let’s move forward towards the relation of Python with ML
What is Machine Learning?
It is one of the most popular fields of AI. The basic concept of this field is to make machine learning from data as human beings can learn from his/her experience. It contains learning models on the basis of which the predictions can be made on unknown data.
But Machine learning is having a complex algorithm and is difficult to manage that is why to have to face certain challenges such as:
Challenges in Machines Learning
While Machine Learning is rapidly evolving, making significant strides with cybersecurity and autonomous cars, this segment of AI as a whole still has a long way to go. The reason behind this is that ML has not been able to overcome a number of challenges. The challenges that ML is facing currently are:
Quality of data
Having good-quality data for ML algorithms is one of the biggest challenges. The use of low-quality data leads to problems related to data preprocessing and feature extraction.
Another challenge faced by ML models is the consumption of time especially for data acquisition, feature extraction, and retrieval.
Lack of specialist persons: As ML technology is still in its infancy stage, the availability of expert resources is a tough job.
No clear objective for formulating business problems
Having no clear objective and well-defined goal for business problems is another key challenge for ML because this technology is not that mature yet.
Issue of overfitting & underfitting
If the model is overfitting or underfitting, it cannot be represented well for the problem.
Curse of dimensionality
Another challenge ML model faces is too many features of data points. This can be a real hindrance.
Difficulty in deployment
The complexity of the ML model makes it quite difficult to be deployed in real life.
These challenges raise the need of why Python is the best language for Machine learning. So let’s have a look at those features of Python which make it the most suitable for Machine learning.
Python for Machine Learning
Python is a popular object-oriented programming language having the capabilities of a high-level programming language. It’s easy to learn syntax and portability makes it popular these days.
Easy to learn and understand
The syntax of Python is simpler; hence it is relatively easy, even for beginners also, to learn and understand the language.
Python is a multi-purpose programming language because it supports structured programming, object-oriented programming as well as functional programming.
A huge number of modules
Python has a huge number of modules for covering every aspect of programming. These modules are easily available for use hence making Python an extensible language.
As being an open-source programming language, Python is supported by a very large developer community. Due to this, the bugs are easily fixed by the Python community. This characteristic makes Python very robust and adaptive.
Python is a scalable programming language because it provides an improved structure for supporting large programs than shell scripts.