Learning is an extensive process, involving several aspects to it. Development of cognitive skills, acquisition of new declarative knowledge, discovering new facts & theories from experimentation are some of the common and significant aspects of learning.
Today, when digital data is the prime source of learning, the human ability to learn and evolve has become slow when compared to machines. That is why there are regressive practices and initiatives to make machines learn and perform tasks, more efficiently than humans.
This idea of implanting the machines with capabilities to learn from large data sets is gaining ground in the market. Researchers and developers are making this possible through Artificial Intelligence and one of its most interesting applications, Machine Learning (ML).
Machine Learning is a method of data analysis wherein a system learns, identifies patterns, and make decisions with minimal human intervention. ML has been here for years and has some interesting use-cases in our day-to-day lives. For example, it is machine learning in the background that’s enabling GPS navigation services to make traffic predictions.
But, how does it all happen? What’s the technique behind making a system learn from a huge data set? The answer is- Machine Learning Models.
In this article, we will understand what machine learning models are, what are the different ways in which ML models learn, and how to build ML models.
ML Models and Training Methods
Machine Learning Model is a mathematical representation of real-world processes. For generating the ML model, a data set is prepared that will be used by a machine learning algorithm for continuous learning. The algorithm discovers patterns in the training data set and uses this to make predictions.
Depending upon different scenarios, inputs, and data types, there are different ways an ML model learns.
In supervised learning, the machines classify objects, problems, and scenarios based on related data that’s fed to them through data sets. Here, the data set comprises of characteristics, patterns height, color, dimensions, etc. of the object/person so that the system classifies them and differentiate between them. In supervised learning, machines are made to learn cognitively, just like humans.
To understand supervised learning, consider this example. There are multiple image databases of cars around the world. The cars in the first two databases are labelled, while cars in the rest of the databases aren’t. Now imagine somebody studying the first two databases (in TBs), classify the car type, and then label cars in other databases.
Instead of involving humans in the task of classifying and labelling the cars, why not train a machine for the same. For example, from the first two databases, a data set of cars can be created with characteristics such as wheels, doors, low ground clearance, etc. This data set can now be used by ML models to identify cars in other databases.
Supervised learning problems can be categorized as:
- Classification Problem: When the output variable is a category, such as “SUV or Sedan”, “Animal or Bird”.
- Regression Problem: When the output variable is a real value, such as “Car”, “Bike”, “Dollars”
Some of the popular ML models that use supervised learning methods are:
- Linear regression for regression problems.
- Random forest for classification and regression problems.
- Support vector machines for classification problems.
In unsupervised learning, the models are made to learn on their own, discover information, create patterns, and then label data accordingly. When this mode of learning is chosen, it allows the system to perform more complex tasks, as compared to supervised learning.
Unsupervised learning problems can be categorized as:
- Clustering: In a clustering problem, inherent groupings in data is analyzed. For example, grouping customers by purchasing behavior.
- Association: Herein, an association is established amongst data objects inside large databases. This involves discovering interesting relationships between a large portion of data. For example, customers who buy product A also tend to buy product B.
In a reinforcement learning model, the machines find the best possible solution or behavior to act in a specific situation. While in supervised learning, data is trained to find an answer to the problems, in a reinforcement learning process, there is no exact answer but a reinforcement agent that decides what should be done to perform a given task. Since there is no data set, the model is bound to learn from its experience.
Building Machine Learning Models
From finance to eCommerce to security, machine learning has its use cases in almost every industry. In one of our recent projects, our AI developers helped RBI to build a mobile app that enables the visually impaired to identify Indian banknotes and distinguish between them.
For doing this, a machine learning model with a supervised learning approach was followed. For this, a proprietary data set of 1,50,000 images of Indian banknotes was created and we trained the ML model using the transfer learning method. Learn more about how AI developers executed this task for India’s central bank, read more.