What is Machine Learning? Definition, Methods
What is Machine Learning?
Machine learning (ML) is the study of computer algorithms which improve automatically through experience and by the use of data. Machine learning algorithms build models based on data which is also known as “training data” to make the predictions and decisions according to that data without being explicitly programmed. Machine learning algorithms are used in a wide variety of applications.
Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
In data science, an algorithm is a sequence of statistical processing steps. In machine learning, algorithms are “trained” to find features in massive amounts of data in order to make decisions and predictions based on new data. The decision and predictions will be more accurate if the algorithm is better.
For example, recommendations from the services like Netflix and YouTube recommend us videos and what to watch; search engines like Google; social media like Facebook, Twitter and Instagram.
Machine Learning Methods:
Machine leaning methods are comes into three categories:
Supervised machine learning:
Supervised machine learning trains itself on a labeled data set. It requires less training data than other machine learning methods and makes training easier because the results of the model can be compared to actual labeled results.
Unsupervised machine learning:
The data has no labels in unsupervised machine learning method. The machine just looks for whatever patterns it can find. The algorithm of unsupervised machine learning can analyze huge volumes of emails and uncover the features and patterns.
Semi-supervised learning:
Semi-supervised learning offers a good medium between supervised and unsupervised machine learning. It uses a smaller labeled data set to guide classification and feature extraction from a larger, unlabeled data set.
What is Reinforcement Machine Learning?
Reinforcement machine learning is a behavioral machine learning model that is similar to supervised machine learning method, but the algorithm isn’t trained using sample data. A reinforcement algorithm learns by trial and error to achieve a clear objective. The example of Reinforcement machine learning is like, the games which can beat human brains by using its algorithms.
What is Deep Learning?
Deep learning algorithms define an artificial neural network that is designed to learn the way the human brain learns. It uses a technique that gives machines an enhanced ability to find even the smallest pattern like the human brain can think. This technique is called a deep neural network. Deep learning models require large amounts of data that pass through multiple layers of calculations.
