Machine learning is an application of Artificial intelligence (AI) that allows systems to automatically learn and refine from that learning while not being programmed explicitly. In other words, the field emphasizes learning – that is obtaining skills or knowledge from experience; this also means, synthesizing useful notions from historical records. As a practitioner in machine learning, you will encounter various types of learning field. So today, we will go over a few different types of machine learning models used in practice today.

Learn Major Difference Between Supervised Vs Unsupervised Learning in detail.

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Supervised Learning

Supervised learning revolves around learning a function that draws an input to an output based on input-output pairs. This algorithm consists of a target variable that must be predicted from a given set of independent variables. We can generate a function that maps input to projected outputs by using the set of variables. The training process continues until the model attains its desired level of precision on the training data.

This is understandable with the concept of function approximation, whereby the training of algorithm is with the end goal being the selection of a function that best describes the input data, or for which a given X makes the best estimation of the Y. This ability to estimate and reach near-accurate results is why supervised learning is heralded as one of the most crucial machine learning practices.
By removing the space for any human error, this creates a relationship where the individual can feed the computer with training data i.e. the input/predictors and the possible outcomes, which then leads to the computer learning the various patterns the said data can reach by incorporating a supervised learning algorithm that reduces the risk of errors and also creates near-accurate results.
Supervised learning algorithms attempt to model relationships and dependencies between the target prediction output and the input features to such an extent that a variety of output values can look upon for any new data based on the relationships that were available with the previous data sets.

Unsupervised Learning

In unsupervised learning, we don’t have any target variable to predict. This algorithm maps inferences from datasets, containing input data without labeled responses. The most frequently used method in this learning is cluster analysis. It is in use for exploratory data analysis to find obscured grouping in data.

Here, the machine learning algorithms go through a process of pattern detection and descriptive modeling whereby no output categories are available for the computer to model relationships on. Instead, these algorithms try to mirror techniques that help mine for rules, detect patterns and summarize/group data points, all of which helps in reaching meaningful insights and a better description of data for the users.

Reinforcement Learning

With this algorithm, the machine is trained to make critical decisions. It is subjected to a condition where it must train itself frequently via trial and error. The machine learns from previous experience and looks forward to absorbing the optimum knowledge to make appropriate business decisions.

Here the focus is on using observations that are available from a variety of environmental interactions that help take actions with the most potential rewards and at the same time help to cut any risks with regards to the data set. Through consistent environmental-centered learning, the agent i.e. the computer learns from its experiences until it has explored the full range of possible states.
We can also view this as a branch of AI since it helps machines and software agents to automatically determine ideal behaviors for specific contexts in an attempt to maximize performance-related goals.

Discover Deep Reinforcement Learning & Its Applications


Now, let’s have a look at some of the different types of Machine Learning Models!

Linear Regression

Based on supervised learning, linear regression performs regression tasks. It hits a target prediction value base on independent variables. It is primarily in use for figuring out the relationship between variables and forecasting. Different regression models vary – based on the type of relationship between dependent and independent variables that they are considering, and the number of independent variables being used. Apart from that, linear regression is one of the most renowned and well-understood algorithms in statistics and machine learning.

Decision Tree

Decision trees are in use for both classification and regression tasks and lie in a non-parametric supervised learning category. They are popular models – normally in use for strategic planning and machine learning and create a model that forecasts the value of a target variable by learning basic decision rules contingent from the data features. Decision trees are instinctive and quite easy to build. However, they hit the skids when it comes to providing accurate results.

By dividing the population into 2 halves and establishing separate parameters for each, the decision tree helps to differentiate and distinguish amongst groups and propose results that are specific to each sub-group present.

Random Forest

Random forest is an ensemble learning technique – a group of decision trees. This technique creates multiple decision trees via bootstrapped datasets of the original data and erratically selects a subset of variables at each phase of the decision tree. Then, it selects the mode of all of the forecasts of each decision tree and predicts the final output. If the number of trees in the forest is high, the output will be accurate and prevent the problem of overfitting.

Neural Network

This multi-layer model is an inspiration by the human brain as it untangles and disintegrates highly complex relationships between variables. What makes neural networks different from other machine-learning algorithms is that their architecture is an inspiration by the neurons in the brain. Just like a brain neuron, neural networks receive input. Based on that input, lets off an output – used by another neuron. Neuron network uses artificial intelligence and simulates this behavior in learning about synthesized data and predicts the outcome. Also, they are very complex and very mathematical.

Naïve Bayes

A classification technique based on the Bayes Theorem assumes independence between predictors. In other words, it assumes that the presence of a particular feature in some specific class is unrelated to the presence of any other feature.
Take the example of an Apple for instance; the naïve Bayes classifier would consider each property of the apple as being different from the other hence creating varying trends for each. We know that an apple is round, shiny, and red in color. These features connect or have certain relationship trends but when put through the naïve Bayes classifier, it would attribute each of these to a separate classification altogether and view them as 3 separate, independent features.

Logistic Regression

Logistic regression was first used in the biological sciences in the early 20th century. But today, it is usually in use when the dependent variable (target) is categorical. It widely scrutinizes and describes the connection between a binary response variable and a set of predictor variables. The main goal of logistic regression is to replicate the mean of the response variable; given a set of predictor variables. However, what differentiates logistic regression from linear regression is that the response variable is binary instead of perpetual.

Following Are Three Types Of Logistic Regression:

Binary Logistic Regression

The target variable can only have 2 possible types i.e. “0” and “1”, which represents a “win” vs “loss” scenario, or “pass” vs “fail”, “dead” vs “alive” and so on.

Multinomial Logistic Regression

This target variable has the potential for 3 or more possible types that are not necessarily ordered like “Object A” vs “Object B” vs “Object C” and so on.

Ordinal Logistic Regression

There are not only targeted variables but also ordered categories such as the categorization of test scores as “very poor”, “poor”, “good”, “very good”, with each category having its own score of 0,1,2,3 and so on.


In a nutshell, there are heaps of complications when you turn to any specific model. This information provides you the most important machine learning models used for practical purposes. Selecting the right model for a specific use case is crucial to get an accurate outcome of a machine learning task. Comparing the performance between different models, evaluation metrics or KPIs are distinct for certain business problems. So, go ahead and choose the best model for production after applying the statistical performance checking.

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