Using Python for Artificial Intelligence & Machine Learning Projects.

Python for Artificial Intelligence
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Python is presently the most preferred language for research and development in Artificial Intelligence and Machine Learning. As per the latest Google trends, in Python for AI has spiked to all-new level as compared to other languages.

But what makes Python the most suitable language for AI and ML? Why are programmers switching to Python to build their ML projects? Read on to find out the reason!

Basic Primer on Machine Learning

Python for Machine Learning

Machine Learning is a branch of Artificial Intelligence that gives a machine the ability to automatically learn and improve without explicit programming. The prime objective of Machine Learning is to create computer models that can access data and use it to learn for themselves.

The process begins with the data such as examples, or instruction, to find patterns in the given data. The computer uses this data to learn automatically without human intervention. The Machine Learning engineers are developing creative and useful Machine Learning projects using Python.

AI’s expansive goal has given rise to various questions and debates to such an extent that no singular definition of the field is universally accepted.  Artificial Intelligence can be seen as a vast brand of computer science associated with developing smart machines that can perform tasks that generally require human intelligence. 

Artificial Intelligence is an interdisciplinary science with multiple approaches. However, with the rapid advancements in ML and deep learning, there is a paradigm shift in virtually every sector of the tech industry. The developers are opting Python for AI across the world.

Also Read: Future of DevOps

Why choose Python for AI?

From development to deployment of a project, Python for AI helps the developers stay confident about the software. Some key features of Python that make it the best fit for ML and AI projects include simplicity, access to useful libraries and frameworks, platform independency, and much more. 

Let us dive a dive a little deeper into each of the benefit of developing Machine Learning Projects using Python.

  • Flexibility

Python for AI is a perfect pick as this language is extremely flexible:

  • Python offers a good option to either use OOPs or scripting.
  • The developers need not compile the source code, that means, they can implement any changes and quickly see the results.
  • Python can be combined with other languages to extend its functionalities and achieve the goals of a project.

Along with that, working on Artificial Intelligence using Python lets the developers choose a programming style they are comfortable with. Some of the common programming styles are the imperative style, the functional style, the object-oriented style, and the procedural style.

Another advantage of the flexibility feature is that it reduces the possibility of errors. The machine learning engineers have a chance to take the situation under control by working in their comfort zone.

  • Extensive selection of libraries and frameworks

A key feature that makes Python for Machine Learning a great programming language is its abundance of libraries and frameworks that facilitate coding and save development time. Python very-well caters the needs of Machine Learning, Deep Learning and Artificial Intelligence.

Some of the widely using libraries of Python for Machine Learning are:

  • NumPy is used for scientific computation
  • SciPy is used for advanced computation
  • Scikit-learn or sklearn is used for data mining and data analysis with algorithms such as linear regression, logistic regression, clustering, classification, and many more
  • Pandas is used for working with data structures.
  • Keras (for deep learning) is a library that allows faster calculations and prototyping

Apart from these, we also have Matplotlib and Seaborn for data visualization. These libraries work along with heavy-hitting frameworks such as TensorFlow, CNTK, and Apache Spark. These libraries of Python for Machine Learning having made the development of projects simpler and faster.

  • Platform independence

Python is a platform-independent language. That means it allows the developers to implement things on one machine and use them on another without any or with minimal changes. Python offers support for multiple platforms such as Linux, Windows, and macOS. The developers are using Python for AI to build standalone executable programs for various operating systems. 

The developers generally utilize Google’s or Amazon’s services to meet their computing needs. The big tech giants use their own machines having powerful GPUs to train their Machine learning projects using Python.

  • Community support

Having a strong community support for a programming language is extremely helpful for any a developer. Python is an open-source platform that has a number of resources open for programmers starting from noobs and ending with the experts.

Multiple python documentations are available online with various Python communities and forums. The machine learning and artificial intelligence engineers can discus stuff with each other and help each other solve their issues. Python for AI has also got a great community support across the world.

  • The simplicity

Python for Machine Learning is widely accepted due to its concise and readable code. The ease of use and simplicity is almost unrivaled, especially for the new developers. You will get several advantages for building Machine Learning projects using Python. 

Machine Learning and Deep Learning depend on extremely complex algorithms to build a project. With Python, a developer does not have to worry about the intricacies of coding. They can shift their focus on looking solutions to problems, and achieving goals of their projects.

Python has an extremely simple syntax that supports faster development. Python for AI allows a programmer to quickly test algorithms without having to implement them.

The readable Python code allows collaborative coding, in case a project changes hands between development teams. It is even more helpful when a project has a great deal of custom business logic or third-party components.

  • A low entry barrier

As a part of the Machine Learning and Artificial Intelligence industry, you will have to deal with a huge amount of data. The low entry barrier of Python allows you to use it for quickly developing AI projects without wasting too much effort on learning the language.

Python for AI somehow resembles the everyday English language, and therefore, it can be learnt easily. The simple syntax of Python for Machine Learning projects makes it easier to work on complex systems, ensuring a clear relation between the system elements.

  • Better data visualization tools

As we have already seen that using Python for Machine Learning is a great option due to its ability to handle the data. A programmer must be able to represent the data in a human-readable form and Python provides him with this power.

Libraries such as Matplotlib and Seaborn allows the programmers to create charts, histograms, and different plots. Using this visual representation, programmers can ease data comprehension. Python for Machine Learning clearly justifies its capabilities by simplifying the visualization process and making it easier to generate results from the data.

Building projects on Artificial Intelligence using Python is not as tough as it may sound. Now that you know why developers are choosing Python for Machine Learning, let us see some of the current applications of AI and ML in our daily life.

Python use cases for Artificial Intelligence and Machine Learning

As we saw, Python offers various features that are extremely helpful for AI and ML. Artificial Intelligence using Python has led to multiple applications in the tech as well other industries across the world. 

Let us have a look at some of the samples of Machine Learning projects using Python.

  • Face recognition
  • Data analysis
  • Transport
  • RPA
  • Emotion bots
  • Marketing and advertising
  • Healthcare
  • Cybersecurity

Facial recognition

Python for Artificial Intelligence

Authenticating personal content (for example, unlocking your phone) is not the only use of facial recognition. Security forces use it to identify criminals and citizens. In the coming future, it can go beyond physical structure to emotional analysis. It will be possible to detect whether a person is stressed or angry.

Data analysis

Artificial Intelligence using Python can perceive patterns in data that humans cannot. It helps the businesses to target the right customers for their product or achieve their business goals. 


AI-guided transport is no longer confined to pages of sci-fi literature. Google’s self-driving car is already popular in the market. The Machine Learning projects using Python will ensure that these Automated Vehicles operate smoothly and efficiently.

Robotic process automation

RPA is the process of automating the rule-based tasks across various industries. RPA allows the people to focus on the critical aspects of their job while leaving the routine aspects to machines.

Artificial Intelligence using Python is expected to carry out tasks that are risky or health-hazardous like bomb diffusion and welding.

Emotion bots

Virtual assistants such as Siri, Cortana, and Alexa have become an inevitable part of our daily lives. They are perfect examples to show how AI comprehends human language. They understand the meaning from context and then make intelligent judgements.

Using Python for AI can also be used to create chatbots that are implemented in a website to provide assistance to the visitors.

Marketing and advertising 

The marketing professionals leave no stone unturned to increase the profit of their business. Machine Learning projects using Python can be used to increase the efficiency of sales and marketing organization.  Personalised advertising, knowledge of customers and their behavior can be easily understood by Artificial Intelligence using Python.


Artificial Intelligence using Python is changing the way the healthcare sector works. Developers are using Python for Machine Learning to build models that can predict and scan diseases, detect injuries, and help people maintain good health.

Multiple AI based projects are being used in the healthcare sector at present. For instance, we have Fathom, an NLP system that is built by Artificial Intelligence using Python. Fathom can analyze the electronic health records and automates the medical coding. 

Another great example is AiCure that ensures that the patients take the right medicine at the right time. With the help of Artificial Intelligence using Python, it has embedded features such as facial recognition, pill recognition, and action recognition. They use IMA for gathering clinically significant data which then can be analyzed by the software.


Artificial Intelligence using Python will definitely bother the hackers. Cybercrime is a rapidly increasing crime across the world. One of the most common cybercrimes is fraud credit cards.

Using Python for AI and RNN, machines can now scan thousands of transactions instantly and predict/ classify them into buckets. It helps quickly identify a case of high risk or fraud.

Also Read: Best IDEs for Python Development

Using Python for Machine Learning projects – The right decision

AI has a significant impact on the world with new technologies emerging all the time. Smart developers are opting Python for AI for the myriad of benefits that make it particularly suitable for machine learning and deep learning projects.

The developers can also use the other programming languages to build AI projects, there is no getting away from the fact that Artificial Intelligence using Python is at the cutting edge, and should be given significant consideration. That is why, you must also choose Python for AI models that will build.

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