Meet the Trio: AI, Machine Learning and Python

A steady rise of fame is seen in the emerging realm of programming languages, and Python is one of those important names that is being favored for numerous applications ranging from web development to scripting and process automation. Artificial Intelligence has been quite profit-spinning among the most advanced areas of computer science with showing no signs of slowing down.

If we see the tech as a whole, it seems to have created a world full of opportunities for application developers. Right from allowing Spotify to recommend artists and songs to users to Netflix to know what shows you must watch next. More importantly, artificial intelligence is extensively used by companies in providing customer service to drive self-service, and improve workflows and employee productivity.

But have you wondered why Python experts require considering AI? The following post emphasizes why Python is the go-to programming language for developers working in the fields of machine learning and deep learning and why you should consider it for your next AI project. Just hang in there!

First, you need to spot the difference between AI, Machine learning and deep learning. Deep learning can be considered as a subset of machine learning whereas AI comprises of machine learning. The process goes like this:

  • AI exhibited by a machine offering an optimal or suboptimal solution
  • Machine learning takes things further by using algorithms to parse data and help in making much-informed decisions
  • Deep learning helps in drawing conclusions in a manner that resembles human decision making.

Where does Python come into play? Why does it act as a good fit for projects involving AI? Let’s find out!  

One of the most crucial aspects that makes python a great choice, in general, is its ample of libraries and frameworks that facilitate coding and save development time. When it comes to scientific computation, NumPy is highly recommended, SciPy for advanced computation, and scikit-learn for data mining and data analysis. Other than this, TensorFlow, CNTK, and Apache Spark are some of the heavy-hitting frameworks that must be taken into account.

Another factor that differentiates python is its concise, readable code and turns out to be pretty much unrivaled when it comes to ease of use and simplicity, particularly for new developers. As you know that ML and DL both heavily rely on extremely complex algorithms and multi-stage workflows. Developers are no longer required to worry about the intricacies of coding which enables them to focus more on finding solutions to problems and achieve the goals of the project.

Last but certainly not the least is the abundance of support. Python is an open-source programming language and is supported by a lot of resources and high-quality documentation. It also boasts a large and active community of developers willing to provide advice and assistance through all stages of the development process.


Artificial Intelligence seems to have a profound effect across the world, all thanks to new applications being evolved all the time. With an extensive selection of machine learning-specific libraries and frameworks, Python simplifies the development process and cut development time. It also reduces the cognitive overhead on developers, freeing up their mental resources. This means there is no getting away from the fact that Python must be given serious consideration.

Last updated:1/11/2019 3:01:48 AM


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