From self-driving cars to streaming platforms that suggest TV shows based on previous behavior—machine learning is everywhere. While it is often confused with artificial intelligence (AI), machine learning is actually a subset of AI. Machine learning algorithms use data to learn and optimize its operations and in turn, develop intelligence over time.

The implementation of machine learning in business operations is a strategic and obvious step. With the vast amount of data generated by business applications every day, machine learning algorithms can help make sense of that data and drive insights from it.

What are machine learning algorithms?

Machine learning algorithms can learn from data and improve on their own from previous experience. The algorithms can learn tasks like mapping input to output, identifying hidden structures in unlabeled data that is stored in the memory. 

Types of machine learning algorithms

1- Supervised Machine Learning Algorithms

With supervised machine learning, the machines learn by example. The programmer provides a known dataset with both desired inputs and outputs, and the algorithm then determines, on its own, the right method to arrive at the required outputs. Though the programmers already know the answer to the problem, it is the algorithm that recognizes the data pattern and makes predictions accordingly. 

As the algorithm starts making predictions, it can be constantly corrected by the programmer until the algorithm is able to achieve a high level of accuracy. The main goal of supervised learning is to make predictions about unseen, unavailable, or future data based on the available sample data.

There are three major types of supervised machine learning algorithms: 

  • Classification: The machine needs to decide which category the data belongs to based on the previous values/ data. For instance, when filtering emails, the machine decides whether the emails are spam or not.
  • Regression: In regression, the output variable is real or continuous — like age or weight of a person. Regression algorithms can be used to predict a person’s age and other related demographics 
  • Forecasting: Forecasting algorithms analyze trends to predict future data based on the past and the present data. These algorithms can be used to predict customer buying behavior and adjust inventory accordingly

Supervised algorithms are used for forecasting sales, retail commerce, and stock trading trends. These algorithms can help businesses analyze market trends and any fluctuations in prices. It is also used to estimate costs in real-time bidding processes in order to keep the budget under required limits. 

Popular supervised algorithms include

Decision trees

A decision tree uses a tree-like graph with conditional control statements that are used to determine the outcome of the problem (or decision). Each node in the tree structure is a feature or an attribute, the branch represents the decision rule, and the leaf of the node is the outcome. In other words, it offers a visual flowchart diagram that is similar to human-level thinking.

Image Credit: Data Camp

Linear regression

Linear regression algorithms help identify the relationship between two continuous variables. While one of the variables can be a predictor or independent variable, the other is usually a dependent variable. The core idea is to determine how one variable can be used to accurately calculate the value of another variable, For instance, if we know the temperature in Celsius, it is easy to calculate the Fahrenheit temperature as well. 

Logistical  regression

Logistical regression algorithms can accurately calculate discrete values like Yes/No, True/False, and Binary 0/1 values based on independent variables. In most cases, they are used to predict the probability of an event occurrence.

K-Nearest Neighbour (KNN)

The KNN algorithm assumes that similar things exist in proximity. It helps group similar data points together according to their proximity to each other. In other words, it estimates how likely it is for a data point to be a member of a group. KNN algorithms are usually used for recommender systems like recommending products on Amazon or videos on YouTube. 

Naive Bayes

It is not one single algorithm, but a family of algorithms that all follow the same principle — Every classifying feature of a dataset is independent of the other. For instance, if we assume that fruit can be considered an apple if it is red, round, and over 3 inches in diameter, then the Naive Bayes algorithm will assume that all of these three features independently contribute to the probability of the fruit being an apple.

2- Unsupervised Machine Learning Algorithms

In unsupervised learning algorithms, the desired outcomes are not already known. While supervised learning uses labeled data, unsupervised learning uses unlabeled data. It can be used for detecting patterns, discovering valuable insights, and identifying information structure

The two major types of unsupervised learning algorithms include:

  • Clustering: These algorithms are mainly used to segment data into groups or clusters based on the internal data pattern. There are no previously known groups, and data is categorized according to the similarities and dissimilarities of the existing data objects.
  • Dimensionality Reduction: When there is a lot of data, dimensionality reduction algorithms are used to filter out relevant information

The more popular unsupervised algorithms include:

Association rule

Association rule helps discover strong relationships between different variables in a large database. For instance, it can be used by eCommerce websites to determine what products their customers usually purchase together in order to make accurate recommendations.

t-SNE (t-Distributed Stochastic Neighbor Embedding)

t-SNE is a non-linear algorithm for dimensionality reduction which is mainly used for visualization of high dimensional datasets. It is used for natural language processing, image processing, and speech analysis.

K-means clustering

K-means clustering helps categorize unlabelled data in relevant groups. It works by first identifying groups within the dataset and then working iteratively to find the right group for every data point.

Companies like Salesforce use unsupervised algorithms to make sense of unlabeled data and extract insights from it. These algorithms can help identify target audience groups according to the behavioral user data which is why they are also used for developing effective marketing campaigns.

3- Semi-Supervised Machine Learning Algorithms

Semi-supervised machine learning algorithms take elements from both supervised and unsupervised algorithms. The machine gets limited sample labeled data to train itself and then it uses that data to label other unlabeled data.

The idea is to use the classification process of the supervised algorithms to identify data assets and the clustering process of unsupervised algorithms to group the data into different parts.

Usually, semi-supervised algorithms are used for image and text analysis. For instance, they can be used to detect any anomalies in MRI or CT scan images.

4- Reinforced Machine Learning Algorithms

Reinforcement learning algorithms focus on creating a self-sustained system that has the ability to improve itself by learning from both the incoming data and the already present labeled data.

In these types of algorithms, a technique called as exploration is followed where an action takes place, the machines observes and learns from the consequence of that action, and then takes that into consideration for the next action.

There are reward signals given after every action which works as the navigation tool of the machine. If the machine performs well based on the past data, it gets a positive reward signal. Otherwise, it gets a negative reward signal.

The more popular reinforced machine learning algorithms include:

Monte-Carlo Tree Search (MCTS)

Monte Carlo Tree Search combines machine learning principles with classic tree search implementations. While a tree search only finds the best action and stops there, MCTS continues to evaluate other alternative decisions that can replace the current best. 

Temporal difference

The temporal difference takes a model-free approach and it learns from trial and error. The algorithm helps predict a quantity which is dependent on future values. The algorithm takes into consideration the changes and differences of the variables to come up with accurate predictions in an iterative manner.  

Q-Learning

Q-learning algorithms use action values or Q-values to improve the learning agent’s behavior. These algorithms are mainly used where only limited or inconsistent information is available. For instance, It is used in video games where the AI reactions are needed for the player’s reactions.

Machine learning algorithms offer a powerful way of extracting insights from all kinds of data, even when the data available is unlabeled.

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