Machine Learning is the subset of Artificial Intelligence and the branch of Computer Science based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human interventions. As a branch of Artificial Intelligence (AI), the main focus of Machine Learning (ML) is developing computer programs to access data and later use it to learn by itself.
So, the main aim of ML is to let computers learn automatically without human interventions and make the necessary adjustments in actions accordingly.
To make better future decisions based on the examples given, we initiate the process of learning with data or observations through examples or direct interactions and then look for patterns in those data.
Today, we are living in a world of humans and machines. Humans have been evolving for ages by learning through their past experiences, while machines are just in their primitive age. It means that the development of machines has just begun. So, the evolution of machines in the future is considered enormous and beyond our imagination’s scope.
At present, we need to program machines or robots to follow our instructions. What if machines start operating by themselves based on past experiences like humans? These things sound fascinating and exciting, right?
The concept of machine learning was developed to solve problems. Because of the growth in more powerful and less expensive processing, use cases, and nearly limitless volumes of data, ML is growing at an accelerating world rate. It deepens the work of Artificial Intelligence, so don’t be confused with AI as machine learning.
It is important to know what makes machine learning work and how it can be used in the future.
We have thousands of machine learning algorithms, and hundreds of them are developed every year. However, every machine learning algorithm has three basic components: representation (how to represent knowledge), evaluation (way of evaluating hypothesis), and optimization (how search processes are generated).
The machine learning process begins by inputting trained data into the selected algorithm. A model is created using these training sets of data. So, when a new input is fed into the algorithm, it makes predictions based on the model. A new set of input data is introduced into the ML algorithm to test the algorithm’s accuracy. If the algorithm is accurate, then the learning algorithm is deployed; if not, the model is trained until we get the desired output.
Generally, there are two common types of Machine Learnings: Supervised Learning and Unsupervised Learning.
Supervised learning uses known sets of data that act as a teacher to train the model. Once the model gets trained based on known data, we can use unknown data to get a new response. Algorithms like Naive Bayes, Linear Regression, Decision Trees are used in supervised learning.
In contrast to supervised learning, unsupervised learning has untrained or unknown data sets. It means that data is not guided. When such data is fed into the machine learning algorithm, it tries to train the model. Then, the model tries to search for a pattern and give the desired response. Fuzzy means, Apriori, and K-means clustering are some of the algorithms used in unsupervised learning.
The application of machine learning is slowly adapting. For example, banks use machine learning to predict the loan-giving mechanism by training the model based on past data from their customers and banking services. Similarly, in social media like Facebook, ML is used for automatic tagging and suggestions. Likewise, self-driving cars, virtual personal assistance, fraud detection, health care, recommendations systems, etc., are some of the application areas of Machine Learning.
Day-by-day, our society is getting digitized through information and technology. Machine Learning is one of the fields that has played a significant role in digitizing society; since it can automate many tasks which previously only humans could perform with their innate intelligence. And this intelligence can be replicated into machines only with Machine Learning. As a result, it has helped businesses and industries to gain profits and avoid unknown risks. On a footnote: if you require assistance in building AI systems using Machine Learning, we’re always here to help.