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Commonly Used Machine Learning Algorithms

The advancements in technology are increasing the demand for machine learning in the business world. To enhance your skills in machine learning, you need to understand the commonly used machine learning algorithms.

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Machine learning is becoming immensely popular because of its ability to derive value from data. Most of the machine learning programs uses a few common algorithms with minor modifications. If you are new to machine learning, you should first gain a good exposure to machine learning algorithms.

Machine learning algorithms learn data and further improve their functions without any form of human intervention. The various learning processes involved include grasping the function and map the input and output, understanding the hidden structure behind unlabeled data, and instance-based learning.

There are so many types of machine learning algorithms that you might easily feel confused. We can group the algorithms based on the learning style or similarity in function or form. Let’s explore more on the various machine learning algorithms prevalent in the business world.


Types of machine learning algorithms

Before you implement machine learning in your organization, get a firm grasp on the benefits of the different machine learning algorithms.

  • Supervised learning

Supervised learning algorithms use direct supervision of the operation. The developer specifies the sample data corpus and also assigns boundaries within which the algorithm should operate. Here you can select what samples to provide the algorithm and what is the desired result. The process is similar to connecting the dots. The main purpose of supervised learning is to make future predictions based on the sample data.

Linear regression, Neural Networks, Logistical Regression, and Gradient Boosted Trees are some common supervised learning algorithms.

  • Unsupervised learning

Unsupervised learning does not require the developer’s direct control. The desired results in this case are unknown and have to be defined. It is used for exploring the information structure, detecting patterns, deriving data insights, and increasing operational efficiency. While supervised learning uses labeled data, the unsupervised learning depends on unlabeled data.

The popular unsupervised learning algorithms are t-SNE, k-means clustering, PCA, and Association rule.

  • Semi-supervised learning

Semi-supervised machine learning algorithms stand in between unsupervised and supervised algorithms. It combines the beneficial aspects of the two and adds its advantages as well. Semi-supervised algorithm uses a defined set of sample data that is labeled to train itself. The process creates a distinct algorithm where both predictive and descriptive aspects of unsupervised and supervised learning are combined. Semi-supervised learning identifies data assets using classification process and groups it based on clustering process.

uClassify is a popular example of semi-supervised learning algorithm.

  • Reinforcement machine learning

Reinforcement machine learning is related to artificial intelligence. It is concerned with developing a self-sustained program that goes through a contiguous sequence of trial and error and improves after each step based on the labeled data and incoming data interactions. Reinforcement machine learning depends on the technique of exploitation/exploration. The process happens in three stages – first the action occurs, then the consequences are noted, finally the results are observed and incorporated in the next action.

Reinforcement learning uses reward signals as a navigation tool to understand the right course of action. A positive reward signal encourages a particular action while negative reward signals discourages certain actions and corrects the algorithm. The overall effort of reinforcement learning is to maximize the positive reward signals and minimize the negative signals.

Commonly used reinforcement learning algorithms are Q-Learning, Monte-Carlo Tree Search, Temporal Difference, and Asynchronous Actor-Critic Agents.


Conclusion

We hope the above discussion about the advanced machine learning algorithms based on learning styles have given you an understanding of the common algorithms and how they relate to each other. Each algorithm solves a different problem set, and by combining these algorithms you can handle a variety of complex tasks in the business world.

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