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Machine Learning is a buzzword in the technology world right now and for good reason, it represents a major step forward in how computers can learn. The need for Machine Learning Engineers are high in demand and this surge is due to evolving technology and generation of huge amounts of data aka Big Data. On an Average, an ML Engineer can expect a salary of ₹719,646 (IND) or $111,490 (US). So, let’s discuss some of the Applications of Machine Learning.
I’ll be discussing the following Applications of Machine Learning one by one:
Now, Google Maps is probably THE app we use whenever we go out and require assistance in directions and traffic. The other day I was traveling to another city and took the expressway and Maps suggested: “Despite the Heavy Traffic, you are on the fastest route“. But, How does it know that?
Well, It’s a combination of People currently using the service, Historic Data of that route collected over time and few tricks acquired from other companies. Everyone using maps is providing their location, average speed, the route in which they are traveling which in turn helps Google collect massive Data about the traffic, which makes them predict the upcoming traffic and adjust your route according to it.
One of the most common applications of Machine Learning is Automatic Friend Tagging Suggestions in Facebook or any other social media platform. Facebook uses face detection and Image recognition to automatically find the face of the person which matches it’s Database and hence suggests us to tag that person based on DeepFace.
Facebook’s Deep Learning project DeepFace is responsible for the recognition of faces and identifying which person is in the picture. It also provides Alt Tags (Alternative Tags) to images already uploaded on facebook. For eg., if we inspect the following image on Facebook, the alt-tag has a description.
If you have used an app to book a cab, you are already using Machine Learning to an extent. It provides a personalized application which is unique to you. Automatically detects your location and provides options to either go home or office or any other frequent place based on your History and Patterns.
It uses Machine Learning algorithm layered on top of Historic Trip Data to make a more accurate ETA prediction. With the implementation of Machine Learning, they saw a 26% accuracy in Delivery and Pickup.
Suppose you check an item on Amazon, but you do not buy it then and there. But the next day, you’re watching videos on YouTube and suddenly you see an ad for the same item. You switch to Facebook, there also you see the same ad. So how does this happen?
Well, this happens because Google tracks your search history, and recommends ads based on your search history. This is one of the coolest applications of Machine Learning. In fact, 35% of Amazon’s revenue is generated by Product Recommendations.
As the name suggests, Virtual Personal Assistants assist in finding useful information, when asked via text or voice. Few of the major Applications of Machine Learning here are:
All you need to do is ask a simple question like “What is my schedule for tomorrow?” or “Show my upcoming Flights“. For answering, your personal assistant searches for information or recalls your related queries to collect info. Recently personal assistants are being used in Chatbots which are being implemented in various food ordering apps, online training websites and also in Commuting apps.
Well, here is one of the coolest application of Machine Learning. It’s here and people are already using it. Machine Learning plays a very important role in Self Driving Cars and I’m sure you guys might have heard about Tesla. The leader in this business and their current Artificial Intelligence is driven by hardware manufacturer NVIDIA, which is based on Unsupervised Learning Algorithm.
NVIDIA stated that they didn’t train their model to detect people or any object as such. The model works on Deep Learning and it crowdsources data from all of its vehicles and its drivers. It uses internal and external sensors which are a part of IOT. According to the data gathered by McKinsey, the automotive data will hold a tremendous value of $750 Billion.
Setting the right price for a good or service is an old problem in economic theory. There are a vast amount of pricing strategies that depend on the objective sought. Be it a movie ticket, a plane ticket or cab fares, everything is dynamically priced. In recent years, artificial intelligence has enabled pricing solutions to track buying trends and determine more competitive product prices.
How does Uber determine the price of your ride?
Uber’s biggest uses of Machine Learning comes in the form of surge pricing, a machine learning model nicknamed as “Geosurge”. If you are getting late for a meeting and you need to book an Uber in a crowded area, get ready to pay twice the normal fare. Even for flights, if you are traveling in the festive season the chances are prices will be twice the original price.
Remember the time when you traveled to a new place and you find it difficult to communicate with the locals or finding local spots where everything is written in a different language.
Well, those days are gone now. Google’s GNMT(Google Neural Machine Translation) is a Neural Machine Learning that works on thousands of languages and dictionaries, uses Natural Language Processing to provide the most accurate translation of any sentence or words. Since the tone of the words also matters, it uses other techniques like POS Tagging, NER (Named Entity Recognition) and Chunking. It is one of the best and most used Applications of Machine Learning.
With over 100 million subscribers, there is no doubt that Netflix is the daddy of the online streaming world. Netflix’s speedy rise has all movie industrialists taken aback – forcing them to ask, “How on earth could one single website take on Hollywood?”. The answer is Machine Learning.
The Netflix algorithm constantly gathers massive amounts of data about users’ activities like:
And a lot more. They collect this data for each subscriber they have and use their Recommender System and a lot of Machine Learning Applications. That’s why they have such a huge customer retention rate.
Experts predict online credit card fraud to soar to a whopping $32 billion in 2020. That’s more than the profit made by Coca Cola and JP Morgan Chase combined. That’s something to worry about. Fraud Detection is one of the most necessary Applications of Machine Learning. The number of transactions has increased due to a plethora of payment channels – credit/debit cards, smartphones, numerous wallets, UPI and much more. At the same time, the amount of criminals have become adept at finding loopholes.
Whenever a customer carries out a transaction – the Machine Learning model thoroughly x-rays their profile searching for suspicious patterns. In Machine Learning, problems like fraud detection are usually framed as classification problems.
So, with this, we come to an end of this article. I Hope you got to know the various applications of Machine Learning in the industry and how useful it is for people. Edureka’s Machine Learning Engineer Masters Program makes you proficient in techniques like Supervised Learning, Unsupervised Learning, and Natural Language Processing. It includes training on the latest advancements and technical approaches in Artificial Intelligence & Machine Learning such as Deep Learning, Graphical Models and Reinforcement Learning.