Machine learning (ML) is an application of Artificial Intelligence (AI). It provides systems the ability to learn and improve from experience without being explicitly programmed. The learning process begins with observations and data such as examples provided by us, direct experience or instructions, in order to look for patterns in data and make better decisions in the future. The primary objective of ML is to enable computers to learn automatically, without any human intervention.
The processes involved in machine learning are similar to that of data mining and predictive modeling, which require searching through data to look for patterns, based on which program actions are adjusted accordingly. Social Media giant Facebook uses machine learning to personalize each members news feed. The software uses statistical analysis and predictive analytics to recognize patterns in user data and use them to populate the News Feed. Search and recommendation engines use also use this technology to personalize online ad delivery in near real time. There are many other use cases in machine learning that includes building news feeds, fraud detection, spam filtering, network security threat detection and maintenance.
Machine Learning methods:
Here are the categories of Machine Learning algorithms.
Supervised Machine Learning Algorithms: These algorithms can add what has been learned in the past to new data to predict future events. By analyzing a known dataset, the algorithm produces a function that is used to predict output values. It can also compare outputs, find errors and make modifications accordingly.
Unsupervised machine learning algorithms: These algorithms are used for unlabeled training data-sets and learn how a function can be inferred to describe its structure.
Semi-supervised machine learning algorithms: These algorithms use both labeled and unlabeled data for training. Systems using this method are able to significantly improve learning accuracy. This method of learning is chosen is the given data requires skilled resources to train it.
Reinforcement machine learning algorithms: This learning method enables the system to interact with its environment by producing actions and discovering errors and rewards. In order to maximize performance, these algorithms allow machines and software to ascertain the ideal behavior to determine which action is best in a specific context.
Combining machine learning with AI can make it even more effective in processing large amounts of data. Machine learning enables analysis of this data to accurately identify profit making opportunities and potential risks.