Where is Machine Learning used?
There’s nothing more tempting than machine learning, right?
The rapid growth and development of Machine Learning have made it a buzzword in the contemporary world’s technology. We are using it without even realizing it in our day-to-day life such as Alexa, Google Assistant, Siri, Maps, and many more. Let’s dive into the most trending real-world applications of Machine Learning being used, before that let’s quickly skim the topics using our most powerful tool: Visualization used by Data Scientists.
1. Speech Recognition
We all once in our life must have used the feature provided by Google- “Search by voice”, which falls under speech recognition and is a well-known machine learning application.
Speech Recognition, often known as “Speech to Text” or “Computer Speech Recognition”, is the process of turning spoken commands into text. Currently, a wide range of voice recognition applications uses machine learning methods extensively. Speech recognition is used by Google Assistant, Siri, Alexa, and Cortana to carry out voice commands.
2. Image Recognition
Computer Vision is the field concerning machines able to understand multimedia like images and videos, is one of the key pillars in the industry of self-driving cars, robotics, and facial recognition.
One of the most popular uses of machine learning is picture identification or image recognition. It is used to recognize things like digital photos, people, places, and items. Have you ever noticed the suggestion given by Facebook to automatically suggest you tag your friends, that’s one of the most popular use cases of image recognition via machine learning. The face identification and recognition used in machine learning is what gives us an automatic tagging recommendation with names whenever we submit a photo of one of our Facebook friends. It is based on the project, “Deep Face” of Facebook which is responsible for face recognition. I will be talking about self-driving cars and robotics in upcoming topics, worry not!
3. Traffic Prediction
What’s your natural instinct when you want to get a route to an unknown place, obviously you will googling it on google maps(isn’t it right?), which offers you the best route and anticipates traffic conditions too. Let’s see how it’s done.
It uses two methods to forecast traffic conditions, such as whether it will be clear, moving slowly, or jam-packed:
- It streams real-time data of your location from the Google Maps app and sensors present in your machine(Mobile).
- It calculates and stores the average time taken on past days at the same time.
Everyone who uses Google Maps contributes to its improvement. In order to boost performance, it receives data from the user and delivers it back to its database. Bonus point, to determine the shortest best route from point A to point b, Google Maps essentially employs two Graph Algorithms: Dijkstra’s algorithm and A* algorithm. A collection of nodes that are identified by edges and vertices makes up a graph data structure.
4. Self-Driving Cars
Self-driving automobiles are one the most intriguing uses of machine learning. Self-driving cars heavily rely on machine learning. The most well-known organization, Tesla, is developing car models by training to recognize objects and people while driving using the unsupervised learning method.
5. Product Recommendations
Amazon, Netflix, and many other entertainment and e-commerce businesses frequently utilize machine learning to recommend products to users. Because of machine learning, whenever we look for a product on Amazon, we begin to see advertisements for the same product while using the same browser to browse the internet. Google also uses a variety of machine learning algorithms to assess user interests and makes product recommendations based on those interests.
Similar to this, machine learning is used to recommend TV shows, movies, and other entertainment options when we use Netflix.
6. Email Spam and Malware Filtering
We nowadays receive hundreds of emails daily and you must have noticed that every new mail that we get is immediately classified as important, normal, and spam. Machine Learning technology enables us to consistently receive essential emails marked with the important sign and spam sign in our inbox.
For example, Google uses Filtering Content, Headline Filter, General Blacklists Filter, Rules-based filters, and Permission filters to classify their email as important and spam.
Multi-Layer Perception, Naïve Bayes Classifier, and Decision Tree are some major algorithms used for virus identification and email spam filtering.
7. Online Fraud Detection
By identifying fraudulent transactions, machine learning makes our online transactions safe and secure. Every time we conduct an online transaction, there may be a number of ways for a fraudulent transaction can occur, including the use of fictitious accounts and identification documents and the theft of money in the middle of a transaction. In order to identify this, Feed Forward Neural Network assists us by determining whether the transaction is legitimate or fraudulent.
Each legitimate transaction has an output that is transformed into a set of hash values, which are then used as the next round’s input. It identifies fraud and increases the security of our online transactions because there is a specific pattern for each legitimate transaction that changes for fraudulent ones.
8. Virtual Personal Assistant
Siri, Cortana, Google Assistant, and Alexa are the leading virtual personal assistant we have, they are assisting us in discovering information using our voice commands, as the name says. The action they are doing in just a mere voice command from us to these assistants, “Make a call”, “Set an alarm”, “Write an email” and many more just seem like a sci-fi movie, but it’s only possible by the miracles of machine learning.
Machine Learning algorithms are a key component of these virtual assistants. These assistants recognize and record our voice commands, transmits them to a cloud server, decode them using machine learning algorithms, and then respond as necessary.
9. Automatic Language Translation
Language plays a major role in society, whenever we are in a new place and aren’t familiar with the native language, no worry at all because machine learning assists us in this by translating the text into the language we know and understand, bridging the gap. Google’s GNMT, Google Neural Machine Learning, is one the best example in this field.
A sequence to sequence learning algorithm with image recognition is used to translate the text from language to language.
10. Medical Diagnosis
The more machine learning is getting into healthcare operations, the more effective and efficient the field is getting. Healthcare organizations have a huge amount of datasets with a huge amount of data points that need to be analyzed and organized thoroughly and machine learning are designed to handle them.
Furthermore, employing machine learning will produce the results much more quickly, enabling the ability to analyze faster, even though healthcare practitioners and machine learning algorithms will most like to arrive at the same conclusion based on the datasets. Another benefit of applying machine learning techniques in healthcare is the partial elimination of human involvement, which lowers the risk of human errors. As monotonous, routine work is where humans make the most mistakes, this is especially relevant to process automation activities.
One of the best examples of machine learning in the healthcare industry is using Optical Character Recognition(OCR) technology on reports and prescriptions, making the data entry more seamless and fast. Therefore this data can be analyzed by the other machine learning algorithms to improve decision-making.
I will be covering all the topics of machine learning in my upcoming articles of mine, please show your support by giving a clap and a follow, Thanks.
