Machine learning is a branch of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
Machine learning is the study of algorithms that modify their behavior as they process new data.
Machine learning algorithms are used in many areas, including but not limited to:
2. Natural Language Processing (NLP)
3. Robotics
4. Facial Recognition
5. Stock Market Analysis
Machine Learning is the science of getting computers to act without being explicitly programmed.
Machine learning algorithms can be broken down into two categories: supervised and unsupervised. Supervised learning algorithms use input data that has been labeled by a human to train the machine, while unsupervised learning algorithms do not have this labeled data and instead look for patterns in the raw data itself.
Supervised learning algorithms can be further divided into regression and classification models. Regression models are used to predict continuous values, such as stock prices over time or how quickly a car will drive on a highway, while classification models are used to predict discrete values, such as whether or not someone has cancer or if a person has purchased something on a website before.
The two main approaches to machine learning are supervised and unsupervised learning. In supervised learning, the data has an underlying structure that is known to a human. This data can be labeled (that is, associated with a label indicating its true value) or unlabeled (no labels are given to indicate what the correct answers are). In unsupervised learning, the data does not have an underlying structure that is known to a human. Instead, algorithms can be used to group together items based on their similarity.
Machine learning algorithms can be grouped into three broad categories linear methods, non-linear methods and kernel methods. Linear methods include classification and regression. Non-linear methods include clustering and anomaly detection kernel methods including support vector machines and Gaussian processes.
