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AI, machine learning and Python: Let’s see how far they can go

Last updated: 12-06-2018

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AI, machine learning and Python: Let’s see how far they can go

The following post emphasises on why technologies such as AI, machine learning turns out to be a big deal for python experts

As an ongoing business trend, the realm of AI is quite profit-spinning. Being considered among some of the most advanced areas in current computer science, the tech seems to be unfolding many fronts across several industry verticals.

Over the past few years, Python seems to be enjoying a steady rise to fame. Favored for applications ranging from web development to scripting and process automation, Python is considered as the popular programming languages across the globe. From LISP to Prolog, Java, Python, C#, any language can be chosen for your next the catch-all term project.

However, you need to keep several factors like personal preference, ease of code, availability of web developers into account. Why Python? The OOPs based, high end interpreted programming language mainly focuses on rapid application development. Due to ease of learning, scalability, and adaptability of Python, it acts as the fastest growing language worldwide. Plus, its ever-evolving libraries make it an excellent choice for projects such as web app, mobile app, IoT, data science or AI.

From startups to MNCs, Python provides a huge bunch of benefits to all. Not being limited to just one activity, its growing popularity seems to have allowed to combine with some of the most complex processes like artificial intelligence (AI), machine learning (ML), and natural language processing and data science.

Now, I have found many of you being confused between AI, machine learning and deep learning. Deep learning can be called as a subset of machine learning, and AI is something that generates a category called machine learning. As the name itself implies, AI is all about the intelligence being exhibited by a machine leading to an optimal or suboptimal solution.

Machine learning is a step further that parses data with the help of algorithms and guide in making informed decisions. Deep learning works in a similar way but is capable of different things. It has the ability to draw conclusions in a manner that resembles human decision making with the help of a layered structure of algorithms. This structure is highly inspired by the neural network of the human brain. As a result, a model that can learn multiple levels of representation that correspond to different levels of abstraction.

So, the question really boils down to this: Why should you waste your time in considering Python as a good fit for projects involving AI? I’ll give you five solid reasons.

1. Less Code — AI is all about algorithms- almost all of them whereas Python provides a great ease to developers when it comes to testing. In fact, it provides a great ease in regards to the writing and execution of the code. It may quite interest you to know that the language has the potential to implement the same logic with as much as 1/5th code in comparision to other OOPs languages; which is simply great! Plus, its interpreted approach allows you to check as you code methodology. 2. Prebuilt libraries — As I said before, Python comprises of lots and lots of libraries depending on the project requirement. For example, you can choose Numpy for scientific computation, whereas when it comes to advanced computing Scipy is the best choice, Pybrain for machine learning. A ‘Modern Approach’ is one of the best libraries that save adequate developer’s time spent on coding base level items. 3. Support — Being completely open source with a great community, python turns out to offer a host of resources available which can get any developer to speed up in no time. The vast community of web developers are active and willing to help in any and every stage of the development cycle. 4. Platform Agnostic — Featuring the flexibility to provide an API from an existing language, Python is also considered as an individual platform. Making a few changes in codes, you can get your app up and running in a new OS. Again this leads to saving development time regarding testing on different platforms and migrating code. 5. Flexibility — Another core advantage offered by the language is flexibility; one can choose between OOPs approach and scripting, as python is suitable for every purpose. In addition to this, it works as a perfect backend and is quite ideal for linking different data structures altogether. For those developers who are struggling between different algorithms, there is an option to check a majority of code in the IDE itself. 6. Popularity — For millennials python is the already the winner. Its versatility, flexibility has the potential to smoothen the learning curve. It may quite interest you to know that looking for Python developers is a much easy thing to do than to hunt for LISP or Prolog programmers, particularly in some nations. Its extensive libraries and active community enhances developing and improves code making it one of the hottest languages today.

• AIMA – Right from Russell and Norvig’s ‘Artificial Intelligence: A Modern Approach’ is all about Python’s implementation of algorithms. • pyDatalog – Logic Programming engine in Python. • SimpleAI – It is easy to use, well documented and tested library. • EasyAI – Simple Python engine for two-players games with AI (Negamax, transposition tables, game solving).

AI seems to have a profound effect on the world and Python turns out to be the good to go programming language of course for the myriad of benefits such as simple syntax and readability making it accessible for the non-programmers. And it also reduces the cognitive overhead on developers, freeing up their mental resources so that they can concentrate on problem-solving and achieving project goals. However, other programming languages can also be used in AI projects; there is no getting away from the fact that Python is at the cutting edge.


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