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What are the five popular Machine Learning Algorithms?

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Machine Learning Algorithms

Machine learning as we all know is a major component that has proven vital in the field of artificial intelligence. Whether your aim is to seek true artificial intelligence or just trying to gain insight from the data that you’ve been collecting, what you need is the basic understanding of machine learning algorithms to move a step forward.

There are different Machine Learning Algorithms for machine learning that we can use. But from the seemingly infinite options, it is difficult for us to choose which one to use. So that is why we thought why not help our learners with this matter and thus here we are with a list of top 5 NPTEL machine learning courses algorithms.

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5 popular algorithms for machine learning

Below are the names and details of the 5 popular Machine Learning  Algorithms.

Linear regression:

From Machine Learning Algorithms Linear regression is a classification method and not a regression method despite its name. It is known best as a predictive modeling approach and has been used by statistics for decades before even the computer was invented. The goal of linear regression is to help us make the most accurate predictions by finding the values of two such coefficients that weight each input variable. The several techniques it uses are linear algebra, gradient descent optimization, and many more.

Logical regression:

From Machine Learning Algorithms Logical regression just like linear regression is another statistical and well-understood method for classification that aims to find the values for two coefficients that weigh each input variable. The main difference here is that it solves problems for binary classification and relies on a logical, non-linear function instead. Hence we can say that the logical regression is used to determine whether the data instance belongs to one class or another. With that, it also helps us provide the reason behind the prediction which cannot be found in the linear regression. Another thing to be kept in mind while using this algorithm is you should limit the correlating data and remove noise.

Machine Learning Algorithms Courses

Classification and regression trees:

This machine learning Algorithms tool is sometimes referred to as CART and a simple form of decision trees. Here the modeled tree is binary where it only uses the algorithms and data structures. Here you can find two types of nodes on this tree.

  • One is the branch nodes that represent a single input variable and also offer a single split point on the variable.
  • And the other one is the leaf nodes that represent the two output variables.

When the machine is in a position to run the algorithm, the prediction plays out by following the branch node splits until reaching a leaf node. And that leaf node is the prediction or class value output that plays a vital part in this whole process.

K-nearest neighbor (KNN):

KNN stands for the K-nearest method where a user has the option to specify the value of K. Unlike the previous algorithms; this one trains on the entire dataset. The goal of the KNN is to help users predict an outcome for a new data instance. The algorithm then trains the machine to check the entire dataset and find the k-nearest instances to the new data instance. Or it can also find the k-number of instances that are quite similar to the new instance. The prediction or output can be one of the two things.

  • Either the mode of most frequent class in a classification problem.
  • Or the mean of the outcomes in a regression problem.

Keep in mind that this algorithm usually employs methods for determining the proximity such as Euclidean distance and Hamming distance.

Naïve Bayes:

From the list of Machine Learning Algorithms Naïve Bayes is a classifier like other beginner algorithms that use different training data in a simpler manner having powerful outputs. It employs the Bayes Theorem of Probability for classifying different content. It is used for calculating the probability of an event that occurs or a hypothesis that is being true based on prior knowledge and then making the model be able to handle two types of probabilities.

  • One is to determine the class.
  • And the other is to determine a conditional probability of each class provided X value.

Conclusion:

These 5 machine learning algorithms are the best ones and are widely used in the IT field. So, know what these 5 tools are and how they work, and never stop learning. Machine learning Algorithms involves computers discovering how they can perform tasks without being explicitly programmed to do so. It involves computers learning from data provided so that they carry out certain tasks. So take any of the courses now and never stop learning.

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