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September 17th, 2020
Why Python is So Good for AI, Machine Learning, and Deep Learning?
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Over recent years, Python has risen to fame and has enjoyed steady popularity as a programming language. When it comes to the most trending technologies like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), Python quickly becomes the top choice. Python is a general-purpose programming language, but despite being that it is widely used for Artificial Intelligence, Machine Learning, and Deep Learning, why? Let’s find out the reasons from this blog

Artificial Intelligence, Machine Learning, and Deep Learning- Do they differ or the same?

Is AI the same as ML? Has Deep Learning in any way related to Machine Learning?

These are all some of the popular questions among most developers and someone who’s interested in learning Python and the technologies developed using them. Let’s break it down:

Artificial Intelligence vs Machine Learning vs Deep Learning

Since Artificial Intelligence, Machine Learning, and Deep Learning have common applications people tend to think that they are the same. But, it is not. For example, Apple’s Intelligent Assistance Siri is an application of AI, Machine learning, and Deep Learning. So how are these technologies related?

What is Artificial Intelligence?

Artificial Intelligence is the science of getting machines to imitate the behavior of humans. Which means getting them to think and make decisions like humans. The machines and robots that have been used in a wide range of fields including healthcare, robotics, marketing, business analytics, and many more are accomplished using Artificial Intelligence.

Although AI is a general term, the type of intelligence used in different technologies are different such as the evolutionary

  • Artificial Narrow Intelligence
  • Artificial General Intelligence
  • Artificial Super Intelligence

Artificial Narrow Intelligence is also referred to as weak AI, involves applying AI only to specific tasks. Alexa, Sophia, Google search engine, self-driving cars, and even the famous AlphaGo are some good examples of narrow intelligence.

Artificial General Intelligence which is also referred to as strong AI, involves machines that possess the ability to perform any intellectual task that a human being can.

Artificial Super Intelligence is referring to the time when the capability of computers will surpass humans.

What is Machine Learning?

Machine learning is a subset of Artificial Intelligence (AI) that focuses on getting machines to automatically make decisions and solve problems by feeding them tons of data.

The data we feed into machines allow them to make decisions. Machines are then trained to detect hidden insights and patterns of the data to interpret, analyze, and process it by using Machine Learning Algorithms. Machines learn to solve a problem by following either of the three approaches given below:

  • Supervised Learning (teach or train the machine using data which is well labeled)
  • Unsupervised Learning (training using unlabeled data and to act on that information without guidance)
  • Reinforcement Learning (learning to behave in a given environment by observing the rewards received on actions)

What is Deep Learning?

Deep learning is a subset of Machine Learning and is a collection of statistical techniques in machine learning that uses the concept of neural networks to solve complex problems. The limitations of Machine Learning are solved using Deep Learning.

Deep Learning is mainly used to deal with high dimensional data. It is based on the concept of Neural Networks and is often used in object detection and image processing.

Deep Learning works by imitating the basic component of the human brain called a brain cell or a neuron. The artificial neuron was developed after being inspired by a neuron.

Although AI, Machine Learning, and Deep Learning are not the same they are all interconnected fields. Artificial Intelligence is aided by Machine Learning and Deep learning aids by providing a set of algorithms and neural networks to solve data-driven problems.

However, Artificial Intelligence is not limited to only Machine learning and Deep learning. It covers a vast domain of fields including, object detection, Natural Language Processing (NLP), robotics, computer vision, expert systems, and so on.

Why did Python gain so much popularity in the fields of AI, ML, and DL?

Python is the choice of language for every core Developer, Machine Learning Engineer, Data Scientist, and more. Let’s find out the reasons why Python is so popular in the fields of Artificial Intelligence, Machine Learning and Deep Learning.

From development to deployment and maintenance, Python helps AI, ML, and DL developers to be productive and confident about the software they’re building. Some advantages of using Python for Machine Learning and AI-based projects include:

  • Less Code
  • Access to great libraries and frameworks for AI and machine learning (ML)
  • Simplicity and consistency
  • Platform independence
  • Flexibility
  • A wide community

Less Code

What makes Python unique and preferable is its simplicity in coding. A developer needs the least effort in coding when compared to Java and other Object-Oriented Programming (OOP) languages. When implementing AI solutions involves tons of algorithms. Python offers support for pre-defined algorithm packages which enables you to code freely as you go. Also, the “check as you code” methodology of Python makes things all the easier.

Access to great libraries and frameworks

Python has hundreds of pre-built libraries to implement various Machine Learning and Deep Learning algorithms. The prebuilt-libraries of Python such as Numpy for scientific computation, Scipy for advanced computing, and Pybrain for machine learning (Python Machine Learning), TensorFlow and PyTorch for Deep Learning making it one of the best languages for AI, ML, and DL.

Simple and Consistent

The concise and readable code of Python is appealing to many developers as it is easy to learn. This easy to the understandable feature of Python makes it easier to build models for machine learning.

Platform Independent

To be able to work on multiple platforms including Windows, macOS, Linux, Unix, etc is one of the biggest advantages of Python. Developers are free to write and implement the code on one platform and then run it on another with minimal changes. Which means no major changes are required to migrate the source code. When it requires transferring the code from one platform to the other you can make use of a package such as PyInstaller that will take care of any dependency issues.

Massive Community Support

Python has a huge community of users worldwide who are always helpful when you encounter coding errors. Apart from a huge fan base, Python has multiple communities, groups, and forums where programmers post their queries about the language and help each other. The presence of these active community of developers is helpful when you have any coding errors to address or any query to be answered and any doubts to be cleared. A few of such groups and communities are Python.org, Stack Overflow, and GitHub, etc.

Bottom Line

AI, ML, and DL have a profound effect in the world we live in, with new solutions coming up day after day. Enterprises have realized that there is no better time than now to invest in these technologies. And therefore, learning Python or getting trained in Python is the need of the hour to start building applications AND systems in it. Considering all the advantages that Python offers, the decision of which programming language to choose for AI, ML, and DL is obvious.

With its amazing features (above mentioned and including the one not mentioned), Python makes the development process of Artificial Intelligence, Machine Learning and Deep Learning-based projects a lot easier, fast, and budget-friendly.

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