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

As an ongoing business trend, AI is quite profit-spinning. It is considered to be among the most advanced areas in current computer science, and the tech seems to be unfolding on 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 a popular programming languages across the globe. From LISP to Prolog, Java, Python, C#, any language can be chosen for your next catch-all term project.

However, you need to keep several factors including personal preference, ease of code and availability of web developers into account.

So, 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.

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The equation of AI, machine learning and Python

From startups to multinational corporations, Python provides an array of benefits to all. Not being limited to just one activity, its growing popularity seems to have allowed the combination with some of the most complex processes such as artificial intelligence (AI), machine learning (ML), and natural language processing and data science.

Now, I have found many are confused over the difference 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 sub-optimal solution.

Machine learning is a step further, it processes data with the help of algorithms and acts as a guide to 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 which resembles human decision making with the help of a layered structure of algorithms. This structure is inspired by the neural network that comprises the human brain. As a result, it forms a model that can learn multiple levels of representation and which correspond to different levels of abstraction.

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Why combine Python and AI?

So, the question 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 makes it easier for developers in testing. In fact, it supports the writing and execution of the code. It may interest you to know that the language has the potential to implement the same logic with as much as a 1/5th of code in required in other OOPs languages. Plus, its interpreted approach allows you a check as you code methodology.
2. Prebuilt libraries — To reiterate, Python comprises lots and lots of libraries, depending on the project requirement. For example, you can choose Numpy for scientific computation, whereas for advanced computing, SciPy is the best choice, and Pybrain is better 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, which can enable any developer to work faster. 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 to be 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 back-end 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 already the winner. Its versatility and flexibility gives it the potential to smoothen the learning curve. It may interest you to know that looking for Python developers is a much easy thing to do than hunting 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.

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Python Libraries for AI

• 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).

Final thoughts

AI seems to be having a profound effect on the world and Python turns out to be the go to programming language,  providing a myriad of benefits, such as simple syntax and readability.  This makes it accessible for the non-programmers. 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 it: Python is at the cutting edge.

Written by Vikash Kumar, Manager of Tatvasoft
Written by Vikash Kumar, Manager, Tatvasoft

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