Machine learning and its use cases
In this blog, we will talk about Machine Learning and its use cases.
First, Let's talk about what exactly is Machine Learning?
What Is Machine Learning?
Arthur Samuel coined the term Machine Learning in the year 1959. He was a pioneer in Artificial Intelligence and computer gaming and defined Machine Learning as a Field of study that gives computers the capability to learn without being explicitly programmed.
In Simple Terms: Machine Learning is an approach to try and achieve Artificial Intelligence through a system that can find patterns in a set of data.
Machine Learning is an Application of Artificial Intelligence (AI) or it is a Subset of AI. it gives devices the ability to learn from their experiences and improve themself without doing any coding.
for eg: Google’s Map- Using the location data from smartphones, Google Maps can inspect the agility of shifting traffic at any time and providing the shortest route available. another example is Email Classification: Gmail categorizes emails into groups Primary, Promotions, Social, and Update and labels the email as important.
Machine learning is a subset of AI. machine learning is the study of making machines more human-like in their behavior and decision by giving them the ability to learn and develop from their own programs. This is done with minimum human intervention, i.e., no explicit programming.
now, the question is how machine learning is different from traditional programming? well, in traditional programming we use a well-written and tested computer program with input data and then machine generate output. but, in machine learning input data along with the output is provided in training the machine and it works out a program for itself.
for better understanding
Applications of Machine Learning
1. Voice Assistants
From Siri and Cortana to Google Assistant, there are plenty of functions when it comes to the personal assistant along with Amazon Alexa and Google Home. From the voice assistant that sets your alarm and find you the best restaurants to the simple use case of unlocking your phone via facial recognition – machine learning is truly embedded in our favorite devices.
These devices and personal assistants are powered by machine learning algorithms. these voice assitants recognize speech (whatever we say) using Natural Language Processing (NPL) convert them into numbers using machine learning, and formulate a response accordingly. these Assistants collect information, understand one’s preferences, and improve the experience based on prior interactions with individuals.
2. App Store and Play Store Recommendations
This feature of both Google’s Play Store and Apple’s App Store. The ‘Recommended for you’ section is based on the applications You have installed on my phone (or previously used).
For example, if You have a few sports and food-related applications – so yours recommended for you section is usually filled with applications that are similar to these apps. I appreciate that the Play Store is personalized to my taste and shows me apps I have a higher chance of downloading.
How does Apple or Google do this? Two words – recommendation engines. This is a very popular concept in machine learning right now.
Using the location data from smartphones, Google Maps can inspect the agility of shifting traffic at any time, moreover, maps can organize user-reported traffic like construction, traffic, and accidents. By accessing relevant data and appropriate fed algorithms, Google Maps can reduce commuting time by indicating the fastest route.
Here are some that you can see on google maps :
- Routes: Go from point A to point B
- Estimated time to travel this route
- Traffic along the route
- The ‘Explore Nearby’ feature: Restaurants, petrol pumps, ATMs, Hotels, Shopping Centres, etc.
Google uses a ton of machine learning algorithms to produce all these features. Machine learning is deeply embedded in Google Maps and that’s why the routes are getting smarter with each update.
The estimated travel time feature works almost perfectly. If it shows ’40 minutes’ to reach your destination, you can be sure your travel time will be approximately around that timeline.
4. Recommendations system and Personalised ads
Social media platforms are classic use cases of machine learning. Like Google, these platforms have integrated machine learning into their very fabric. From your home feed to the kind of ads you see, all of these features work thanks to machine learning.
A feature which we regularly see is ‘People you may know. This is a common feature across all social media platforms, Twitter, Facebook, LinkedIn, etc. These companies use machine learning algorithms to look at your profile, your interests, your current friends, their friends, and a whole host of other variables.
The algorithm then generates a list of people that match a certain pattern. These people are then recommended to you with the expectation that you might know them (or at least have profiles very similar to yours).
I have personally connected with a lot of my professional colleagues and college friends thanks to LinkedIn’s system. It’s a use case of machine learning benefitting everyone involved in the process.
The ads that we see work in a similar fashion. They are tailored to your tastes, interests, and especially your recent browsing or purchase history. If you are a part of a lot of data science groups, Facebook or LinkedIn’s machine learning algorithm might suggest machine learning courses.
Pay attention to this next time you’re using social media. It’s all machine learning behind the curtains!
5. Customer Support Queries (and Chatbots)
You will understand this at a very personal level if you’ve ever dealt with customer support (and who hasn’t?). Those dreaded phone calls, the interminable wait, the unresolved query – it all adds up to a very frustrating user experience.
Machine learning is helping remove all these obstacles. Using concepts of Natural Language Processing (NLP) and sentiment analysis, machine learning algorithms are able to understand what we’re saying and the tone which we are saying it in.
We can broadly divide these queries into two categories:
- Voice-based queries
- Text-based queries For the former, machine learning algorithms detect the message and the sentiment to redirect the query to the appropriate customer support person. They can then deal with the user accordingly.
Text-based queries, on the other hand, are now almost exclusively being handled by chatbots. Almost all businesses are now leveraging these chatbots on their sites. They remove the impediment of waiting and immediately provide answers – hence, a super useful end-user experience.
Machine Learning changed our life by making it a lot easier. also with some of the AI and ML trends, we are expecting more growth in technologies. We are living in a golden age of machine learning. You must have imagined the vast and endless possibilities of this wonderful field.
Google says Machine Learning is the future
Machine learning played a significant role in our lives and will continue.