Machine Learning is a field of AI that uses computer algorithms to improve the performance of decision-making processes. It is a subset of AI that uses data to improve decision making.
Machine learning algorithms learn from data, in order to improve the accuracy of future predictions.
There are a number of different machine learning algorithms, each with their own strengths and weaknesses.
Machine learning deals with the design and development of algorithms that can learn from and make predictions on data. These algorithms have been able to achieve impressive results in a variety of tasks, ranging from image recognition to text translation.
One of the most promising applications of machine learning is its potential to help us make better decisions. By understanding how data can be used to predict future events, we can make informed decisions that can lead to better outcomes.
For example, imagine you're a doctor trying to diagnose a patient. You could use machine learning to develop an algorithm that can predict which diseases a patient is likely to develop, based on their medical history and other factors. This would allow you to focus your attention on the patients who are most at risk, and potentially save lives.
Of course, machine learning is not without its challenges. One of the biggest challenges is ensuring that the algorithms we develop are fair and unbiased.
importance-Machine_learning_alzoid
image source: wikimedia commons
There are many types of machine learning, but in this post we will focus on two main types:supervised learning and unsupervised learning.
Supervised learning is where the machine is given a set of training data, which is a set of examples of the data that the machine will be learning to recognise. The machine then learns how to recognise the examples based on the training data, and can then be used to recognise new data.
Unsupervised learning is where the machine is given a set of unlabeled data, which is data that has been not been given any specific labels. The machine then learns how to recognise the examples based on the
