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Growth of Python in Machine Learning

Machine learning is a branch in computer science that studies the design of algorithms that can learn. The typical tasks are concept learning, function learning or predictive modeling. Others include clustering and finding predictive patterns. Usually, these tasks are learned through available data that were observed through experiences or instructions. Python is an OOPs based high level interpreted programming language that is highly useful and focused on rapid application development. It is a perfect choice for artificial intelligence and DRY (Don’t Repeat Yourself). IT works perfectly as a glue language as well which is used to connect the existing components together. Python’s support for ever-evolving libraries makes it a good choice for any project whether Web App, Mobile App, IoT, Data Science or AI.

Python is used in a variety of purposes, ranging from web development to data science to DevOps.  The usage of Python is such that it cannot be limited to only one activity. Its growing popularity has allowed it to enter into some of the most popular and complex processes like Artificial Intelligence (AI), Machine Learning (ML), natural language processing, data science etc. The question is why Python is gaining such momentum in AI? And the answer lies below:

The most common areas where python can be used for machine learning are Search Volumes and Job Ads

Python machine learning in search volumes

Search volume indicates the search for information that is required for going deeper into a particular topic. Google too provides a tool called Google Trends that provides insights into the search volumes for keywords over time.

Python Machine learning for jobs is growing

The best example for this is Indeed which is a job site and like Google Trends they show the volume of job ads for particular keywords. It looks for occurrences over time of selected terms in job offers.  It gives an indication of what skills employers are seeking.  Note however that it is not a poll on which skills are effectively in use.  It is rather an advanced indicator of how skill popularity evolve (more formally, it is probably close to the first order derivative of popularity as the latter is the difference of hiring skills plus retraining skills minus retiring and leaving skills).

Less code and artificial intelligence

Artificial intelligence involves algorithms – lots of them. Python provides ease of testing.  Writing and execution of codes are also easy. It can implement the same logic with as much as one-fifth of the code as compared to the other Oops languages – thanks to its interpreted approach which enables you to check as you code.

Above all, it has gained a lot of momentum recently and is a good choice for applications based on AI, IoT or Data Science.

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