Machine learning is one of the most powerful forces in technology. Its development is shaping the forefront of the future in industries in artificial intelligence. Machine learning refers to the automated process by which machines extract meaningful patterns in data. Without machine learning, artificial intelligence as we know it wouldn\’t be possible.
In 1959, MIT engineer Arthur Samuel coined the term \”machine learning\” and described machine learning as a \”Field of study that gives computers the ability to learn without being explicitly programmed.\”
Machine learning is one of the most powerful forces in technology. Its development is shaping the forefront of the future in industries in artificial intelligence. Machine learning refers to the automated process by which machines extract meaningful patterns in data. Without machine learning, artificial intelligence as we know it wouldn\’t be possible.
In 1959, MIT engineer Arthur Samuel coined the term \”machine learning\” and described machine learning as a \”Field of study that gives computers the ability to learn without being explicitly programmed.\”
Types of learning algorithms
The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.
Supervised
Human experts acts as the teacher where we feed the computer with training data containing the input/predictors and we show it the correct answers (output) and from the data the computer should be able to learn the patterns.
Unsupervised learning
Here there’s no teacher at all, actually the computer might be able to teach you new things after it learns patterns in data, these algorithms a particularly useful in cases where the human expert doesn’t know what to look for in the data.
Semi-supervised Learning
In the previous two types, either there are no labels for all the observation in the dataset or labels are present for all the observations. Semi-supervised learning falls in between these two.
Reinforcement learning
It allows machines and software agents to automatically
determine the ideal behavior within a specific context, in order to maximize
its performance. Simple reward feedback is required for the agent to learn its
behavior; this is known as the reinforcement signal.
Processes and techniques
Various processes, techniques and methods can be applied to one or more types of machine learning algorithms to enhance their performance.
Feature learning
Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data
Sparse dictionary learning
Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions, and is assumed to be a sparse matrix.
Anomaly detection
Anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.
Decision trees
Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item\’s target value (represented in the leaves).
Association rules
Association rule learning is a rule-based machine learning
method for discovering relationships between variables in large databases. It
is intended to identify strong rules discovered in databases using some measure
of \”interestingness\”.
Models
Artificial neural networks
An ANN is a model based on a collection of connected units or nodes called \”artificial neurons\”, which loosely model the neurons in a biological brain.
Support vector machines
Support Vector Machine takes numeric input and binary output. It is based on finding a linear plane with maximum margin to separate two class of output. Categorical input can be turned into numeric input as before and categorical output can be modeled as multiple binary output.
Bayesian networks
It is a dependency graph where each node represents a binary variable and each edge (directional) represents the dependency relationship.
Genetic algorithms
A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem.
