Practical Machine Learning


Artificial Intelligence(AI), a very old subject in computer science, explores how to make machines intelligent. Machine learning(ML) is a subset of AI, which focuses on teaching machines how to learn patterns, based on given a set of data. In short, try to replicate, how humans learn new skills or make decisions.

How are businesses using it ?

Good news is, AL/ML is not restricted to Sci-Fi movies OR big corporations.

Due to explosive growth in hardware capabilities, open source libraries and ability to pay per use model to access this computing power, AI/ML is now within reach of small businesses.

Small businesses and even individually run businesses, can now use machine learning to not just getting more output in less time, but also reduce manual errors in doing so.

Here are some of the examples how practical machine learning is being to improve various businesses

  • Have you seen some version of “You make also like” section in E-Commerce. Or maybe in online streaming platforms based on your interest ? That is and example of Unsupervised Machine Learning.
  • Have you seen videos of auto-driving cars or taken a ride or own one ? That is an example of Reinforcement Machine Learning.
  • Have you heard of programs predicting factory output ? Or maybe price of car/house ? Or maybe preventative health recommendations based on your other readings ? That is and example of Supervised Machine Learning.
  • Have you heard of programs pre-processing patient scans so that expert radiologist/cardiologist/neurologist/opthalmologist can only look at edge cases ? That is and example of Deep Learning.

How can we help you ?

If you are a business, which wants to cut the hype, and see how to use practical machine learning, we can help build such models based on custom dataset available with you.

Check out links to some of the case studies we have built using public data, to see what is possible :

  1. Heart Failure Prediction based on 11 input parameters (83% accuracy)
  2. Detect SARS-COV2 presence using CT Scan (96% accuracy)

These models were trained on a small data set. Hence these numbers can be further improved by training the model on larger datasets and for a longer time.

Interested in getting other details like false positive/negative rates etc. for above model, Feel free to write to us .