Introduction to Categories of Machine Learning Algorithms

Efficiently built Artificial Intelligence software is as astonishing as magic. Think of the movie suggestions from Google or the prediction by software about a person in picture posted at Facebook. The predictive analytics of these systems are strikingly close to reality. In fact, the AI is ahead of fantasy because the technology is capable of training itself through machine learning algorithms by using the data sets and past experiences.

The amateur researchers and entrepreneurs should unravel the marvel of AI because the technology is all set to shape the future of humans. The algorithms enable the observer lacking AI knowledge to perceive them as self-teaching software.

The flowchart below demonstrates the broad categories of ML algorithms followed by an essential overview along with at least two examples for each category.

Machine Learning Algorithms




Supervised Machine Learning Algorithms

As the name indicates, these algorithms require extensive input from developers. An AI engineer must define the boundary and initial conditions along with the labeling of data-sets to make the distinction. They outline the answers from a selected range to make the software learn patterns. The human trainers bias the training to ensure that the software makes accurate predictions once the training concludes.

The supervision offers a predefined function which pertains to the datasheet so that the software becomes capable of making closest predictions.

Classification

This process requires the labeling of data to define the bias. The labeling is followed by the segmentation which involves grouping of similar data groups. The grouping enables the software to make predictions by comparing the incoming data with previously defined segments.

For instance, the characters ‘a’ written in a range of varying fonts are placed in the same group so that the software recognizes the character when asked based on the comparison with distinct groups.

Regression

The supervision through regression requires a continuous stream of incoming data. This way, the software will be able to understand the most frequent patterns of data stream. As a result, the data makes predictions of upcoming streams accordingly.

Examples

  • Linear Regression
  • Nearest Neighbor
  • Decision Trees
  • Naïve Bayes

Unsupervised Learning

The training method does not introduce bias. Instead, the software is allowed to extract the patterns itself. This method is used when the data scientists do not want to keep the results in certain boundaries. For instance, it is inadvisable to limit the prediction of consumer behavior for certain industry within fixed margins because the trends vary time and again.

Supervised learning allows biasing because the administrators already know the expected results. Learning the software enables it to sort the data in various categories. Unsupervised learning discourages biasing because the range of outcomes is unknown.

Another distinctive feature is the kind of data. Unsupervised learning is performed on unstructured data, in essence – unfiltered data. Conversely, Supervised Machine learning Algorithms act on structured data.

Clustering

Clustering is vital for many machine learning projects the segmentation of data based on the patterns. Data-sets in the same group must have at least one similarity which is common in every group member while distinguishing from other groups. Clustering is similar to classification in Supervised Learning. The only difference is the bias which is absent in Unsupervised learning.

Dimensionality Reduction

While clustering is the classification of unstructured data, dimensionality reduction involves the removal of noise to extract the most useful information. The algorithms in these categories are primarily concerned with optimization of the algorithm but attempting to reduce the number of random variables.

Examples

  • K-means Clustering
  • Association

Reinforcement Learning

This learning mechanism uses the rewarding and restraining approach. The software analyzes the environment, adopts the practices which improve the results. It also eliminates those which negatively affect the outcomes.

The method requires repeated actions on different data-sets and varying actions on similar data-sets. In this way, the system applies every possible action on each of the datasheets. This extensive approach enables the system to improve itself over the time.

The policy enables the software to build upon its past experiences with the datasets when it interacted with them. The algorithms repeat the actions on data as long as the outcome does not meet the ideal behavior. The repetition of actions is called the Markov Decision Process.

Google Brain Team maintains a comprehensive reinforcement learning framework called TensorFlow.

Reward System

The reward signals differentiate the activities which are desired by the administrators from the ones which exploit the system. The former is called positive reward while the latter as a negative reward. The system is made to adopt the positive rewards immediately and drop the negative ones to prevent them from exploiting the outcomes. In case of negative signals, the action is repeated with different datasets. This reinforcement allows the system to acquire the most ideal state.

Recommendation System

This system is the reason behind suggestions while doing online grocery or searching for anything online. The system assesses the past behavior of user and recommends the closest result which the user may want. Facebook friend suggestions also use the recommendation system algorithms.

For readers with technical knowledge, these algorithms are based on graphs – one of the primary data structures. The algorithms connect the dots between different groups of data associated with the search and recommend the most relevant options.

Examples

  • Q-Learning
  • Deep Adversarial Networks

Semi-Supervised Learning

Many AI engineers consider this approach as a separate category of machine learning algorithms. In essence, Semi-Supervised learning uses the same approaches as Supervised and Unsupervised learning approaches. However, it combines the two prior approaches labeling only a limited data-set. This labeling enables the model to train the unlabeled or unsupervised data. The resultant data indicates an aggregate of the two models.

Keep Exploring

The article presents only a brief overview of aspirants intending to develop machine learning applications, its categories and the essential algorithms in each category. This branch of the AI offers much more. The performance of each algorithm varies on different platforms. Furthermore, AI engineers decide to apply certain algorithm by considering a number of factors related to data-sets. The ML algorithms enhance the performance of mobile applications by manifolds.

Contact us today to learn more about applications whose features are based on machine learning.