Machine Learning is the science of teaching machines how to learn by themselves. It is the science of getting computers to learn and act like humans do, and improve their learning over time in an autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.
Machine Learning refers to the area of artificial intelligence which enables IT systems to identify patterns based on algorithms and data to develop a suitable solution. A machine learning algorithm, also called model, is a mathematical expression that represents data in the context of a problem, often a business problem. The aim is to go from data to insight.
Machine Learning is becoming popular around the world and businesses are trying to integrate it into their functions. New opportunities for aspiring data scientists are growing multifold. Machines need a way to think and this is exactly where machine learning models help. The machines capture data from the environment and feed it to the machine learning model.
Use of Machine learning in today’s life
To give an example of machine learning in our daily lives it is worth to mention that Machine learning is the process that powers many of the services we use today—recommendation systems like those on Netflix, YouTube, and Spotify; search engines like Google; social-media feeds like Facebook and Twitter; voice assistants like Siri and Alexa.
Some of Machine Learning tasks are mentioned in the following list:
- Finding, extracting and summarizing relevant data
- Making predictions based on the analysis data
- Calculating probabilities for specific results
- Smartphones detecting faces while taking photos or unlocking themselves
- Adapting to certain developments autonomously
- Optimizing processes based on recognized patterns
From the voice assistant that sets your alarm and finds 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. Voice Assistants such as Apple’s Siri, Google Assistant, Amazon’s Alexa, Samsung’s Bixby, and other similar voice assistants use machine learning to recognize what you say and search for the most related results on the Internet.
The ‘Recommended for you’ section on Google Play or AppStore is based on the applications you have already installed on your phone or previously used.
Machine learning is a fast-growing trend in the health care industry, because of the wearable devices and sensors that can use data to assess a patient’s health in real-time. The technology can also help medical experts analyze data to identify trends or red flags that may lead to improved diagnoses and treatment.
Smartphones use a technique called facial recognition to unlike itself by detecting our faces. And the core idea behind facial recognition is powered by machine learning.
Everyone has used Google Search and most of us use it multiple times on a daily basis. Google uses machine learning to power its Search engine by serving us with the best results upfront.
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 that we regularly see if ‘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.
Businesses are using machine learning to better analyze threats and respond to adversarial attacks. These use cases extend to both offline threats as well as online (bank frauds, financial threats, etc.). Banks and other businesses in the financial industry use machine learning technology for two key purposes: to identify important insights in data, and prevent fraud. The insights can identify investment opportunities, or help investors know when to trade.
With the help of Machine Learning, economic data can be turned into money. Companies that rely on Machine Learning or Machine Learning methods are not only able to increase the satisfaction of their customers, but also to achieve cost reductions at the same time. Through Machine Learning, customer wishes and needs can be evaluated and the following marketing measures can be personalized. This optimizes the customer experience and increases customer loyalty.
What are the types of Machine Learning?
Machine learning types can be classified into three different categories such as Supervised, Unsupervised and Reinforcement. It differs based on the nature of the data it receives.
Supervised learning is a process like you are learning under someone’s supervision. The data is labeled to tell the machine exactly what patterns it should look for. In supervised learning, the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. Supervised learning is commonly used in applications where historical data predicts likely future events. For example, it can anticipate when credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim.
Unsupervised learning uses information which is neither classified nor labeled. There are no correct answers and there is no teacher. Algorithms are left to their own devices to discover and present an interesting structure in the data. The machine tries to structure and sort the data entered according to certain characteristics.
Reinforcement learning is a method to interact with its environment producing actions and discover errors. A reinforcement algorithm learns by trial and error to achieve a clear objective. It tries out lots of different things and is rewarded or penalized depending on whether its behaviors help or hinder it from reaching its objective. Imagine say you have an image of an apple to the machine and then the system identifies it as a ball that is wrong, so you provide the negative feedback to the machine saying that it is an apple image. So the machine will learn from the feedback and finally, if it comes across an image of an apple, it will be able to classify it correctly.