Machine Learning is an area of artificial intelligence (AI) that involves the development of algorithms and statistical models that enable computer systems to improve their performance on a specific task through data automatically. 

In layman’s language, it is a way of teaching a machine to recognize patterns and make decisions based on data without being explicitly instructed for each decision. 

Application of Machine learning includes image and speech recognition, natural language processing, fraud detection, recommendation systems, and predictive modeling in fields such as finance and healthcare. 

Machine learning algorithms can analyze huge chunks of data and make accurate predictions, which adds value to different sectors such as finance, healthcare, and marketing. It can automate decision-making processes, saving time and improving efficiency. These algorithms can personalize content and recommendations to individuals based on their behavior, preferences, and history. Machine learning models can continually learn and improve with new additional data, making them more accurate. Machine learning algorithms have the advantage of scalability.

The machine learning algorithm can have its disadvantages too. Machine learning models can be biased towards certain groups or outcomes if the training data provided to the algorithm is biased. A lot depends upon the type of data the algorithm is provided. The data being incomplete or of poor quality with uncertain or irrelevant data can affect the model. These models can overfit the training data and perform poorly on new data. Machine learning carried out on a large scale of data with numerous variables can be complex and will require the expert of a professional.