We can't deny that the internet plays an important role in our personal and professional lives! We are all reliant on technology nowadays. We used to rely on all manual methods to achieve our goals almost a decade ago, and we never expected that we would be thinking about machine learning applications in this period. We never imagined that we could examine the exact condition of traffic on a road before leaving a location to reach our intended destination. It was difficult to think 10 years ago that we could order food with just a few clicks! In fact, have you ever considered saying "Ok Google" or "Hey Siri" and having someone speak to you and do what you want?
Machine Learning is a hot topic in the tech industry right now, and for good reason: it represents a significant advancement in the way computers learn. Machine Learning Engineers are in high demand, thanks to advances in technology and the development of massive volumes of data, sometimes known as Big Data. An ML Engineer may expect a salary of $719,646 (IND) or $111,490 on average (US). So, let's have a look at some Machine Learning Applications.
Top 10 Applications of Machine Learning
Machine Learning aids in the improvement of corporate decisions, productivity, disease detection, weather forecasting, and much more. In essence, a machine learns from its inputs automatically. The following are some of the top machine learning examples:
- Social Media Platforms -
Automatic Friend Tagging Suggestions in Facebook or any other social media site is one of the most prevalent Machine Learning applications. Face detection and image recognition are used by Facebook to automatically discover the face of a person that matches its database, and it then advises that we tag that person using DeepFace.
DeepFace, a Facebook Deep Learning project, is in charge of recognising faces and determining who is in the photo. Alt Tags (Alternative Tags) are also provided for photographs that have already been submitted to Facebook. If we look at the alt-tag on the following image on Facebook, we can see that it contains a description.
- Google Translate -
Remember how tough it was to converse with people or identify local spots when everything was printed in a different language when you arrived at a new place?
Those days are no longer with us. Google's GNMT (Google Neural Machine Translation) is a Neural Machine Learning system that employs Natural Language Processing to produce the most accurate translation of any sentence or words. It works on thousands of languages and dictionaries. Other approaches such as POS Tagging, NER (Named Entity Recognition), and Chunking are used since the tone of the words is important. It is one of the most popular and widely used Machine Learning applications.
- Fraud Detection -
Online credit card fraud is expected to reach $32 billion in 2020, according to experts. That's more than Coca-Cola and JP Morgan Chase together made in profit. That is a cause for concern. One of the most important Machine Learning applications is fraud detection. Due to a multiplicity of payment methods - credit/debit cards, cellphones, various wallets, UPI, and much more – the number of transactions has skyrocketed. At the same time, criminals have honed their skills in spotting loopholes.
When a consumer completes a purchase, the Machine Learning model examines their profile in detail, looking for worrisome tendencies. Issues like fraud detection are generally posed as classification problems in Machine Learning.
- Self driving cars -
Here's one of the coolest Machine Learning applications. It's here, and it's already being used. Machine Learning is critical in the development of self-driving cars, and I'm sure you've heard of Audi or Tesla. NVIDIA, a hardware manufacturer, is the market leader and their current Artificial Intelligence is based on Unsupervised Learning Algorithm.
NVIDIA asserted that their model was not trained to detect people or any other objects. The model employs Deep Learning and gathers data from all of its vehicles and drivers. It makes use of internal and exterior sensors, which are part of the Internet of Things.
- Google Maps (Traffic Alerts) -
Now, Google Maps is most likely THE programme we use if we need directions or traffic information. I was driving to another city the other day and chose the freeway, and Maps said to me, "Despite the Heavy Traffic, you are on the Fastest Route." How does it know that, though?
It's a combination of people who are now using the service, historical data collected over time on that route, plus a few tactics learned from other organisations. Everyone who uses Google Maps is contributing their location, average speed, and route, which helps Google collect vast data about traffic, allowing them to predict impending traffic and change your route accordingly.
- Transportation and Commuting -
If you've ever used an app to book a taxi, you've already utilised Machine Learning. It gives you a tailored application that is exclusive for you. Based on your History and Patterns, it automatically recognises your location and offers options to go home, office, or any other frequent location.
It makes a more accurate ETA forecast by layering a Machine Learning algorithm on top of Historic Trip Data. They witnessed a 26 percent increase in accuracy in Delivery and Pickup after implementing Machine Learning.
- OTT Platforms or Online Video Streaming -
Netflix is without a doubt the king of the internet streaming world, with over 100 million users. Netflix's meteoric rise has stunned the movie industry, prompting the question, "How on earth could one single website take on Hollywood?" Machine Learning is the answer. Just like Netflix there are many other OTT platforms like Amazon prime video, Disney+ Hotstar, TVF Play and many more.
Netflix's algorithm is constantly collecting large amounts of information about users' activities, such as:
- Whenever you pause, rewind or fast forward.
- On which days do you watch content? (TV Shows on Weekdays and Movies on Weekends).
- The time and date you watch shows.
- When you take a break and leave the show (and if you ever come back).
- The number of ratings given (about 4 million every day), and the number of searches (about 3 million per day).
- Scrolling and Browsing Patterns.
And there's a lot more. They collect this information for each of their subscribers and use it in their Recommender System as well as a variety of Machine Learning Applications. That's why their customer retention rate is so high.
- Dynamic Pricing -
The challenge of determining the appropriate price for an item or service is an old one in economic theory. There are a plethora of pricing options to choose from, depending on the goal you're pursuing. Everything is dynamically priced, whether it's a movie ticket, a plane ticket, or taxi prices. Artificial intelligence has made it possible for pricing systems to track purchase trends and determine more competitive product prices in recent years.
For Example - How does Uber figure out how much your ride will cost?
Surge pricing, a machine learning model dubbed "Geosurge," is one of Uber’s most prominent implementations of machine learning. Prepare to pay twice the standard fee if you're running late for a meeting and need to book an Uber in a congested neighbourhood. Even for flights, if you travel over the holiday season, rates are likely to be double what they were previously.
- Virtual Personal Assistants -
Virtual Personal Assistants, as the name implies, help people obtain useful information when they ask for it via text or voice. Here are a few of the most important Machine Learning applications:
- Text to speech conversion
- Speech to text conversion
- Natural Language Processing
- Speech recognition
Simply ask a simple inquiry such as "What is my schedule for tomorrow?" or "Show my forthcoming flights." To collect information, your personal assistant searches for information or recalls your connected questions. Personal assistants have recently been deployed in Chatbots that are being used in numerous food ordering apps, online training websites, and commuting apps.
- Product recommendations -
Let's say you look at an item on Amazon but don't buy it right away. However, the next day, when viewing videos on YouTube, you notice an ad for the same thing. You go to Facebook and see the identical ad there as well. So, how does this take place?
Because Google analyses your search history and proposes adverts based on your search history, this happens. This is one of the most fascinating Machine Learning applications. In reality, Product Recommendations account for 35% of Amazon's revenue.
Wrapping Up
So there you have it: some of the most well-known real-world examples of machine learning applications. If these applications have piqued your interest and you're interested in pursuing a career in machine learning, now is the time to enrol in the top machine learning courses, certifications, and training available. These courses will teach you how to use supervised and unsupervised learning techniques in machine learning.
Career prospects for Machine Learning professionals will undoubtedly grow as the digital world progresses and new technology developments are widely recognised. So begin your adventure into the realm of technology by studying about machine learning.
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