Machine Learning Definition | What Is Machine Learning

In this article, you’ll learn what machine learning is, different types of machine learning, real world applications, and difference between machine learning and AI.

What is Machine Learning?

Machine Learning is a branch of Artificial Intelligence (AI), and this technology is used to get more accurate results by learning and improving based on the data and algorithms. It transforms a machine into a thinking device that uses its generated data to learn and improve itself from experience and get better results. 

Who is the Father of Machine Learning?

Geoffrey Everest Hinton (born on 6th December 1947). He is a British-Canadian cognitive psychologist and computer scientist who mainly worked on machine learning neural networks. He is also known as the father of machine learning.

After researching ways of using neural networks for machine learning, memory, and symbol processing, he authored & co-authored over 200 publications. 

Types of Machine Learning

Machine Learning is a branch of Artificial Intelligence that allows computers to think and improve based on data and experience. Machine learning solves problems like regression, classification, forecasting, clustering, etc. It comes with different methods & ways of learning, and it’s divided into four types:

Let’s discuss this in detail!

1. Supervised Machine Learning

Supervised learning is the easiest type of machine learning. In this type of learning, machines are trained on labeled data. So that machine can predict the output based on the label and its means. First, we train machines about the input and output data, and later we ask machines to identify which data is input/output by using labels. 

Let’s understand with an example:

If we have an input dataset of cats & dogs images. So first, we will train the machines to understand the image of a cat & dog with their shape, size, and body parts. After completing the training, we showed cats & dogs images to the machine and asked them to separate cats images & dog images.

Based on the training, collected data about the input machine will respond and give the desired output. This is the simplest way of learning supervised learning. 

2. Unsupervised Machine Learning

This learning is opposite to supervised learning, which means there’s no prior training provided to the machines, and machines will predict the output without supervision. In unsupervised learning, we’ll input a group of datasets in different properties and let machines understand the data and organize data in a human-readable manner. With unsupervised learning, it’s easy to categorize the unsorted data based on the data similarities, patterns, and differences.

In this process, the machine will learn itself based on understanding the data and its parameters to categorize. 

Let’s understand with an example:

If there’s a basket of fruits images and we put that image into the machine, then it’s completely unknown data for the machine and given a task to find the same fruits and categories. First, the machine will learn about the object, its patterns, shape, colors, and differences, and once it learns about the data, it categories accordingly. 

3. Semi-Supervised Learning

This machine learning lies between supervised learning and unsupervised learning. In this process, the machine will get some data with labels and some without any labels, and the machine needs to find out and categorize based on its learning and analyzing the objects’ pattern, shape, and size. Most of the data is unlabelled, but some data is labeled and lets machines identify the data based on data, experience, and learning. It’s mainly used to overcome the drawbacks of supervised and unsupervised learning algorithms. 

Let’s understand with an example – When a student is under the supervision of an instructor at college, then it’s supervised learning. If the student is self-studying, then it’s unsupervised learning, and when a student is learning itself under the supervision of an instructor, then it’s semi-supervised learning. 

4. Reinforcement Learning

Reinforcement learning is advanced learning that is mainly processed based on feedback. In this process, machines automatically learn by doing so many hits & trials, taking actions, experiencing and learning from the experience, and later taking better action to get good results.

In reinforcement learning, there are no labeled data available for machines, and it requires self-analysis and performing tasks based on feedback and improving the process.

Let’s understand with an example:

When a child learns so many things by experiencing itself day-by-day based on the experiences and feedback, it’s easy to grow and work better. It’s known as reinforcement learning. 

Real-Life Applications of Machine Learning

Machine Learning is a growing field, and its demand is growing in various industries. Around 41% of organizations using AI reported reduced business cost, according to Mckinsey. Let’s see some real-life examples of using machine learning: 

1. Image Recognition

Image Recognition is the most popular example of machine learning in the world. Machines can easily identify objects in an image based on their shape, size, and colors. In the real world, you’ve seen this technology in facial recognition systems that come in devices that scan human faces, and once it matches, then unlock the device. 

2. Speech Recognition

Now, machines can transform speech into text, and time-frequency bands segment it. In this process, machines will recognize the voice, understand the speech and convert it into text based on the frequency. Some popular examples are Google Home and Amazon Alexa. 

3. Medical Diagnosis

Many physicians use chatbots and speech recognition features to understand the pattern and act accordingly. 

4. Predictive Analytics

Machines will identify the objects and arrange them in categories based on analyzing their shape, size, and colors to categorize them and find the probability of legitimate or fraudulent. 

Difference between AI and Machine Learning

Artificial Intelligence (AI)Machine Learning (ML)
Artificial Intelligence is a high-tech technology that enables machines to get & apply knowledge for any human work. Machine Learning is used to give machines the ability to think and improve according to the results. 
AI helps to increase the chance of success but not accurately. ML aims to increase accuracy but is not sure about success. 
It works like a computer to do a task more effectively.Machines learn from the data and improve themselves to act more effectively.
AI is decision-making.Machines learn new things from the data.

Examples of Machine Learning

Machine Learning technology is growing rapidly, making human life easier and more dependent on technology. In today’s world 30% of companies globally using AI in one of their sales process, according to venture harbour. Here are some amazing examples of using machine learning in day-to-day life:

1. Face Recognition

Face Recognition is a popular technology that we use on our smartphones daily. You’ve noticed that now smartphones are coming with face scanners to unlock the devices and the technology behind this face recognition is machine learning. 

2. Traffic alerts using Google Maps

Billions of people use Google Maps for their daily uses because it helps them find the destination, distance, and fastest route to reach the location. Google Maps uses machine learning technology that collects information from different sources and analyzes and predicts the best possible route for a user. 

3. Chatbot

Almost all areas of a chatbot are widely used software like banking, medical, education, health, etc. These chatbots help customers resolve their queries based on frequently asked questions.

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