In this post, we will try to decipher what is ML (Machine Learning) and what are its types?
In layman terms-
Machine Learning (ML) gives computers the ability to learn without being explicitly programmed.
In simple-technical terms-
In ML you construct algorithms that can learn from and make predictions on data.
_____________________________________
Machine learning tasks are typically classified into three broad categories, depending on the nature of the learning "signal" or "feedback" available to a learning system:
[1] Supervised learning
The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
[2] Unsupervised learning
No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
[3] Reinforcement learning
A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). The program is provided feedback in terms of rewards and punishments as it navigates its problem space.
[4] Semi-supervised learning
Between supervised and unsupervised learning is semi-supervised learning, where the teacher gives an incomplete training signal: a training set with some (often many) of the target outputs missing. Transduction is a special case of this principle where the entire set of problem instances is known at learning time, except that part of the targets are missing.
_____________________________________
ML tasks can also be categorized into following:
[1] Deep learning - the application of Artificial Neural Networks (ANN) to learning tasks that contain more than one hidden layer.
[2] Shallow learning - ML tasks with a single hidden layer.
_____________________________________
Another categorization of ML tasks arises when one considers the desired output of a machine-learned system:
[1] Classification
In classification, inputs are divided into two or more classes, and the learner must produce a model that assigns unseen inputs to one or more (multi-label classification) of these classes. This is typically tackled in a supervised way. Spam filtering is an example of classification, where the inputs are email (or other) messages and the classes are "spam" and "not spam".
[2] Regression
In regression, also a supervised problem, the outputs are continuous rather than discrete.
[3] Clustering
In clustering, a set of inputs is to be divided into groups. Unlike in classification, the groups are not known beforehand, making this typically an unsupervised task.
[4] Density estimation
Density estimation finds the distribution of inputs in some space.
[5] Dimentionality
Dimensionality reduction simplifies inputs by mapping them into a lower-dimensional space. Topic modeling is a related problem, where a program is given a list of human language documents and is tasked to find out which documents cover similar topics.
In layman terms-
Machine Learning (ML) gives computers the ability to learn without being explicitly programmed.
In simple-technical terms-
In ML you construct algorithms that can learn from and make predictions on data.
_____________________________________
Machine learning tasks are typically classified into three broad categories, depending on the nature of the learning "signal" or "feedback" available to a learning system:
[1] Supervised learning
The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
[2] Unsupervised learning
No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
[3] Reinforcement learning
A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). The program is provided feedback in terms of rewards and punishments as it navigates its problem space.
[4] Semi-supervised learning
Between supervised and unsupervised learning is semi-supervised learning, where the teacher gives an incomplete training signal: a training set with some (often many) of the target outputs missing. Transduction is a special case of this principle where the entire set of problem instances is known at learning time, except that part of the targets are missing.
_____________________________________
ML tasks can also be categorized into following:
[1] Deep learning - the application of Artificial Neural Networks (ANN) to learning tasks that contain more than one hidden layer.
[2] Shallow learning - ML tasks with a single hidden layer.
_____________________________________
Another categorization of ML tasks arises when one considers the desired output of a machine-learned system:
[1] Classification
In classification, inputs are divided into two or more classes, and the learner must produce a model that assigns unseen inputs to one or more (multi-label classification) of these classes. This is typically tackled in a supervised way. Spam filtering is an example of classification, where the inputs are email (or other) messages and the classes are "spam" and "not spam".
[2] Regression
In regression, also a supervised problem, the outputs are continuous rather than discrete.
[3] Clustering
In clustering, a set of inputs is to be divided into groups. Unlike in classification, the groups are not known beforehand, making this typically an unsupervised task.
[4] Density estimation
Density estimation finds the distribution of inputs in some space.
[5] Dimentionality
Dimensionality reduction simplifies inputs by mapping them into a lower-dimensional space. Topic modeling is a related problem, where a program is given a list of human language documents and is tasked to find out which documents cover similar topics.
Sources:
Datasciencecentral.com/profiles/blogs/machine-learning-summarized-in-one-picture
en.wikipedia.org/wiki/Machine_learning
No comments:
Post a Comment