Machine learning: A simple explanation of its functioning
The word that is creating a buzz around today’s technology world is machine learning. Starting from handy online recommendation of Netflix to the concept of futuristic self-driving cars, all big tech advancement of the artificial intelligent era is powered by machine learning.
Let’s first understand what machine learning is
Machine learning can be called the subset of Artificial Intelligence that empowers a system to learn and improve automatically from experience sans being explicitly programmed. It focuses on developing algorithms that enable a machine to learn for themselves using data and make decisions with minimal human intervention.
The learning process starts with data observation from our examples or instructions where the machine can look for patterns in the data to make a better decision in the future by itself.
Now what’s the methods applied in machine learning
Supervised and unsupervised learning are the two most widely adopted methods to train machine learning algorithms. There are some other methods too and, we have explained it in detail.
Supervised learning algorithm
In supervised learning, the algorithm applies the past experiences to new data using labelled examples to predict future events. The learning algorithm receives inputs and the correct outputs, the right answers corresponding to all the inputs, and it learns by finding out the errors when comparing the actual output with the correct output. And the method is modified accordingly.
Unsupervised learning algorithm
The dataset used in unsupervised learning has no historic labels and the correct output is not given to the learning algorithm. The algorithm needs to figure out the output and its main objective is to find out a hidden structure from the unlabelled data.
Semi supervised machine learning algorithm
In the semi-supervised method, the algorithm is trained using both labelled and unlabelled data. Semi-supervised learning is normally applied if the cost of labelling data is too high. Here a small amount of labelled data is used with large unlabelled data.
Reinforcement machine learning algorithm
In this method, the agent or the learner interacts with the environments through some actions that yield errors and rewards. This method allows the systems to automatically learn the ideal behaviour within a specific context and maximise its performance. The major objective for the agent is to perform actions that can maximize rewards over a given period.
Machine learning can analyse a massive amount of data. When it is likely to deliver a faster and accurate result for prediction, it may also require the investment of time and resources to train it properly.
How machine learning is different from Deep learning
Deep learning is a part of machine learning where functions of the algorithms are much similar, but, they consist of numerous layers. Each layer provides a unique interpretation of the data it feeds on. These networks of algorithms are also called ANN or artificial neural networks. It attempts to function the same way a human brain neural networks does.
The major difference between machine learning and deep learning is the way the algorithm receives data. Machine learning algorithms mostly rely on structured data, whereas deep learning depends on the layers of ANN.
Machine learning algorithm produces outputs with the understanding of labelled data. If it doesn’t attain at the desired output, it may need to be retrained through manual intervention. In the case of deep learning, it doesn’t require human intervention because the artificial neural networks put data through different hierarchical concepts where it can learn from its own mistakes.