Machine Learning For Beginners
Have you ever shopped online? So while checking for a product online, have you noticed it recommending a product similar to what you are looking for? Or did you notice that “people bought this product also bought this” combination of products?. How are they providing these recommendations to us? This is because of machine learning.
What is Machine Learning? Machine Learning is a subset of artificial intelligence which focuses mainly on learning and making predictions based on the machine’s experiences.
What does it do? It enables the computers to make data-driven decisions rather than being explicitly programmed for carrying out a certain task. These algorithms are designed in a way that they learn and improve over time when are exposed to new data.
What is Machine Learning – Evolution of Machines
As you know, we are living in the world of human beings and machines. Humans have been evolving and learning from their past experience for millions of years ago. On the other hand, The new era of robots and machines have just begun. Now you can consider it in a way that currently we are living in the primitive age of machines, while the future of machine is enormous and is beyond the scope of our imagination.
In today’s world, the machines have to be programmed before they start following our instructions. But what if the machine started learning on their own from their experience feel like us, work like us, do things more accurately than us?
How does Machine Learning Work?
Machine Learning algorithm is created using training datasets to create a new model. When new input data is introduced to the ML algorithm, it makes predictions on the basis of the model.
The prediction is evaluated for the accuracy and if the accuracy is acceptable, the ML algorithm is deployed. If the accuracy is not acceptable, the Machine Learning algorithm is trained again with an augmented training dataset.
Types of Machine Learning
Machine learning is sub-categorized to three types:
What is Supervised Learning?
Supervised Learning is, where you can consider the learning is guided by a teacher. We have a dataset which acts as a teacher and its role is to train the models or the machines. Once the model gets trained it can start making predictions or decisions when data is given to it.
What is Unsupervised Learning?
The model learns through observations and finds structures in the data. Once the model is given a dataset, it automatically finds relationships and patterns in the dataset by creating clusters in it. What it cannot do is add labels to the cluster, like it cannot say this a group of oranges or mangoes, but it will separate all the oranges from mangoes.
Let’s say we presented images of oranges, bananas, and mangoes to this model, so what it does is, based on some patterns and relationships it creates several clusters and then divides the datasets into those clusters. Now if a new data is provided to these models, it adds it to one of the clusters which was created.
What is Reinforcement Learning?
It is the ability of an agent to interact with the environment and find out which is the best outcome. It follows the concept of trial and hit method. The agent is rewarded or penalized with a point for a correct or a wrong answer, and on the basis of the positive reward points gained, the model trains itself and it gets ready to predict the new data presented to it.