Tag: supervised learning course

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In recent years, artificial intelligence (AI) and machine learning (ML) have gained immense popularity and are expanding their horizons rapidly. It is said that technologies driven by ML and AI are yet to show their true potential. Hence, companies from every sector are adopting AI and ML and trying to integrate various powerful technologies. This is why, the future of artificial intelligence and machine learning is quite promising.

One of the subfields of artificial intelligence and machine learning is supervised learning. To learn and understand more about this sub-area, one can get enrolled in online artificial intelligence and machine learning course. This will help to create a vivid understanding of supervised learning. Subsequently, an individual can commence a successful and lucrative career in supervised learning.

What do you mean by supervised learning?

Supervised learning is one of the promptly growing sub-arenas of artificial intelligence and machine learning. This branch deals with issues faced in the real world. It also has the potential to solve these problems. Notably, problems like the segregation of spam mail and messages can be easily done with the help of this amusing technology. Supervised learning consists of various datasets and algorithms that can predict the results automatically.

If an individual is willing to commence a career in supervised learning, then he/she must have detailed knowledge about it. For this, one can opt for online supervised learning training. This will help you to bag lucrative job offers.

How does supervised learning function?

Supervised learning acts on the training set. This dataset trains a model for the desired output which is later yielded by the model. A training dataset mainly consists of various inputs and correct outputs. This procedure assists the machine or model to comprehend real-life problems over sufficient time. The training dataset adjusts all the errors until it is minimised and accurate. 

Supervised learning segregates problem detection into two distinct categories. And, they are:

Classification

Classification can assign tested data to accurate categories with the assistance of an algorithm. It traces, defines, and labels different data entries within the dataset of the model. The few most commonly used classification algorithms are k-nearest neighbour, linear classifiers, decision trees, random forests, and support vector machines (SVM). These algorithms are user-friendly and easy to use. 

Regression

Regression is used to comprehend the link between dependent and independent variables. It is usually used to create projections or statistics. Some of the most used regression algorithms are logistical regression, linear regression, and polynomial regression.

What are the various algorithms of supervised learning?

Supervised learning has various types of algorithms. And, these have been precisely discussed below:

K-nearest Neighbour

KKN is also known as k-nearest neighbour. This segregates data points with the help of proximity to other data. This algorithm tends to assume that the same data points can be located exactly near each other. So, it calculates the distance between two data points with the assistance of Euclidean distance.

Random Forest

One of the most famous and flexible supervised learning algorithms is Random Forest. It can be used for classification as well as regression. Initially, it obtains all the unrelated decision trees and unifies them together to obtain more precise data projections  

Support Vector Machine (SVM)

One of the most popular supervised learning models is Support Vector Machine (SVM). This was created by Vladimir Vapnik and can be used for regression as well as classification. This model creates a hyperplane, where distance is maximised between two classes of data points. This hyperplane is also known as the decision boundary.

Logistic Regression

Logistic regression is generally used when there is a binary or dual output like ‘yes’ and ‘no’. This regression model is built to comprehend the bond between the inputs. However, the main task of Logistic Regression is to resolve problems like spam identification.

Linear Regression

The main task of Linear Regression is to find the link between a dependent variable and independent variables. It is also responsible for foreseeing future results. There are two kinds of Linear Regression, namely Multiple Linear Regression and Simple Linear Regression. For an instance, when there is only one dependent variable and one independent variable, it is known as Simple Linear Regression. However, when there is more than one independent variable, it is referred to as Multiple Linear Regression. The method of least squares is used to plot the line of best fit for different linear regressions. 

Conclusion 

Imarticus Learning is offering an IIT artificial intelligence course that covers the entire portion of supervised learning. This is an online course that is led by experienced instructors. The course will open many lucrative job opportunities for those who want to commence a career in this domain. Thus, without any delay, get yourself enrolled in this online course. 

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Top 7 examples of supervised learning algorithms

Supervised learning algorithms are great for solving problems with a large amount of training data. The supervised learning algorithms are great for classifying high-dimensional data representing high-dimensional vectors and matrices. This post will discuss seven examples of supervised learning algorithms.

Linear Regression

It is a supervised learning algorithm that relates the value of one or more independent variables to the value of a dependent variable. The goal is to find linear combinations of these independent variables that can also predict values for your dependent variable.

The process behind linear regression is simple: you have some data, which might be either a set of samples or entire population distribution.

You then choose one or more continuous variables and their corresponding values and use them as inputs into a linear equation whose coefficients represent how much each input contributes to predicting your outcome variable’s value.

Decision Trees

Decision trees are based on the principle that if you have enough examples of your training data set, then you can use these examples to create one tree per decision variable in your problem. In this case, multiple branches would come out of each node, representing different possible outcomes or predictions made by our model using each input variable.

Support Vector Machines (SVM)

Support vector machines (SVM) are supervised learning algorithms in binary classification. The SVM is also known as a kernel-based classifier. It uses the concept of high-dimensional data points to determine which of two classes (or categories) will be most beneficial for further analysis and prediction.

Logistic Regression

It is a supervised learning algorithm that can use for classification, binary classification, and multi-class classification. Given its probability density function, it predicts the probability of an event occurring. 

Nearest Neighbor

Nearest neighbor is a supervised learning algorithm used to classify data. 

The algorithm uses information about each point in your datasets, such as its x and y coordinates and color or shape, to determine how similar each point is to itself (its Euclidean distance). The value of each feature used by this algorithm will vary depending on what you’re trying to do with your data set.

Gaussian Naive Bayes

The Naive Bayes model is a generative model. (Gaussian) Naive Bayes assumes that each class has a Gaussian distribution. The basic idea behind GNB is that we have a set of training data (a bunch of examples), and we want to predict what event will happen next in our new example. 

Random Forest

Random Forest is a supervised learning technique that uses multiple decision trees to make predictions. It gets used in many fields, including biology and machine learning. 

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