Machine Learning uses methods that can be used to accomplish clear goals and objectives to obtain information or value from data. In our smartphones, computing devices, blogs, etc., we can see its growing implementation. As data sources proliferate, algorithms for machine learning emerge not only as effective as manual programming, but also as a cost-effective solution. Company entities of all kinds have benefited from it. But there are several facts about Machine Learning that, if understood, help one to better understand it. Key Machine learning facts Machine learning (ML) and Artificial Intelligence (AI) are not the same things There is a vital distinction that most people forget between the AI and ML. In order to perform tasks and produce results that would otherwise require human intelligence, AI machines are programmed. Some examples include facial recognition, recognition of speech, decision-making, and translation of languages. ML systems, on the other hand, are designed to make them ‘learn’ how to achieve an outcome based on data sets that are fed into them. A generalization that goes beyond existing data One of the Machine learning facts widely overlooked by those beginning with ML is linked to its ultimate objective of generalizing results. Generalization of machine learning data relates to the successful implementation of the concepts learned on new data through an ML model. ML cannot determine data relevancy While ML can accomplish a lot of great things for you, there is one job you need to do. This is to ensure that the data that is entered into it is appropriate for the particular mission. Since ML picks up on any pattern of data, it runs the risk of storing unnecessary information, which can negatively affect its outcome. Overfitting- The bugbear of machine learning The problem emerging when an ML system knows the training data too well is overfitting. So much so that it acquires and accepts any minor variation as a definition. These principles do not extend to new data and hinder the capability of these systems to generalize data. Feature engineering – Key to a high performing machine learning model The main aim of feature engineering is to prepare proper and structured datasets that meet the machine learning algorithms’ requirements. Nothing influences the outcome of ML algorithms more than the characteristics of the datasets, according to Luca Massaron, a well-known data scientist. More data does not increase the risk of pattern hallucination It’s a common opinion that higher data amounts raise the likelihood of ML pattern hallucination. But an expert in a Machine learning company is productive in reducing the chance of hallucination caused by mining higher attributes of related entities.