Machine Learning

Machine Learning (ML) is a subfield of AI that deals with the construction and study of algorithms that can learn from and make predictions on data. In text analytics, ML is used to automatically extract insights from unstructured text data by building models that can identify patterns and trends.

How is Machine Learning different from other similar terms ?

Machine Learning is often confused with other similar terms such as artificial intelligence (AI) and data mining. However, there are some key differences between these terms:

• Machine Learning is a subfield of AI that specifically deals with the construction and study of algorithms that can learn from and make predictions on data.
• Data mining is a process of extracting valuable information from large data sets. It does not necessarily involve the use of algorithms that can learn from data.
• Artificial intelligence is a broader field that includes both machine learning and data mining.

Procedure of Machine Learning

There are three main steps in the machine learning process:

• Data preprocessing: This step involves cleaning and preparing the data set for modeling.
• Model training: In this step, the ML algorithm is trained on the preprocessed data set.
• Model evaluation: This step assesses how well the ML model performs on unseen data.

Types of Machine Learning Algorithms

There are two main types of ML algorithms: supervised and unsupervised. Supervised learning algorithms are used when the training data set includes labels or target values. Unsupervised learning algorithms are used when the training data set does not include labels.

Some common examples of supervised learning algorithms include decision trees, support vector machines, and linear regression. Some common examples of unsupervised learning algorithms include k-means clustering and association rules.

Benefits of Machine Learning

Machine learning can be used to automatically extract insights from large data sets that would be too difficult for humans to analyze. It can also be used to build models that can make predictions about future events. ML is a powerful tool that can help organizations make better decisions and improve their operations.