Modern day applications of machine learning

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 Following are some of the the applications of machine learning in various domains that I have come across :

Advertising

Advertising or advertisement forms one of the major business models in the world presently. Most of the modern day businesses, be it social media sites or writing apps, all earn and sustain themselves through advertising content to their users based on relevancy (or maybe not! ha-ha). As such, it is obvious that machine learning finds its use in this domain in multiple forms:

Predicting click rate

ML is used in predicting the number or percentage of viewers who will actually click on an ad displayed to them.

Recommending appropriate ads 

ML is used in recommending appropriate ads to users based on their interests and activity. Read recommendation engines to understand how it is actually done.

Medicine and healthcare

Medicine and healthcare forms one of the most important sectors of any place. As such whatever improves its quality needs to be invested in as soon as possible. ML finds applications in healthcare domain in the following ways:

Disease Diagnosis

Diagnosing diseases using pretrained and tested ML models using patient symptoms and reports is one of the main uses of machine learning in the healthcare sector. A simple web search will lead you to many applications of this sort, designed to track illnesses using AI.

Disease Treatment

Yes, you read it right! ML models are being used not only to diagnose disease, but also to predict the right course of treatment based on patient history and condition. The final decision is still in the hands of the doctor, though. However, the day is not far when robots powered by ML models will perform surgeries and operate on you. You might already have heard of robot-assisted surgeries being performed these days.

NLP

NLP stands for natural language processing. It is basically making computers understand human language (like English) and be able to talk back to human beings. 

AI bots

You have already used all these apps on your phone which talk to you, haven't you? Google Assistant, Alexa, Siri, etc., You can ask them any question and they will search and frame you a decent answer to it. At the back of it all, it all started with ML.

Sentiment Analysis, Spam detection

ML is also used in extracting sentiment from texts, let us say company reviews. Further application also include emotion detection and spam detection from the plethora of messages received. This is typically useful in case of call centres where you can potentially save a lot of money by implementing an automated system which sorts out messages received in the right bucket to avoid wastage of human resources. For example, the human executive will only have to review and reply to those messages which are not spam (because the ones which are would be rejected by the ML system). Just imagine the time saved! 

Chatbots

Whenever you visit a website these days, notice a lot of them will have automated chatbots which will ask if you need help. Including support through chatbot is very common these days.

Business 

Machine learning has extensive use in the business sphere as well, particularly business intelligence where you try to make decisions based on data. Following are some glimpses of the use: 

Trends and forecasting

Ml finds extensive use in modelling business trends (including factors like seasonality of products) and then making forecasts for further business decision making. Read time series for further details (beyond the scope of this article).

Customer churn rate prediction

Customer churn rate basically means how many of your customers will leave you. If you find in advance that a particular customer is about to leave you, you might be able to change their minds by , let us say, giving discounts or special offers. This is where ML models are used in the business setting.

Climate change

ML, largely related to statistics, obviously finds use in creating, developing and analysing climate change models which are carried around everywhere by climate activists. There is a huge debate regarding the accuracy of these models. However, it is important to understand that these models presently are the only way we have to measure climate change to some decent extent (I think!). I will leave further discussion up to climate experts (with PHDs and stuff) to decide.

Stock market prediction

Now this is a debatable application. Even though, you will find stock prediction written as a standard application of machine learning everywhere but I don't think it actually works accurately. I mean, if it did, wouldn't the people doing it be super rich? Instead it is used to excite people into ML. At best, I think ML provides with good analysis reports in this domain.

This is as far as I will go. However, the list of applications is non-exhaustive.

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