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6 Aspects of Machine Learning - Finnegan Pierson - Blog

6 Aspects of Machine Learning

People in a multitude of industries are beginning to see how beneficial machine learning is to their respective fields. Machine learning involves computer algorithms. These algorithms can enhance specific learning processes without the need for human intervention. Some of the most notable machine learning applications include self-driving cars, neurocomputers, digital assistants, embedded systems, and expert systems. Individuals are using machine learning to improve e-commerce systems, better their banking and investment decisions, build robots and drones, create effective advertising and marketing campaigns, and streamline certain medical processes. Check out this list of six aspects of machine learning.

  1. Neural Networks

Neural networks are a set of algorithms that behave like the human brain. These networks yield neuron-like structures that signal one another. Neural networks are ideal for image recognition because they can manage complex processes incredibly well. Note that these networks do indeed have their drawbacks. It takes time to train them since a plethora of layers are involved. Also, they need a great deal of power to function. Pieces of neural network hardware, also known as neural network chips, are ideal for artificial intelligence and deep learning processes.

  1. Linear Algorithms

One sort of machine learning algorithm is the linear algorithm. There are two kinds of linear algorithms. One kind is linear regression. It results in a line that produces an optimal fit through a set of data points. Linear regression is fairly easy to comprehend, but it has its limitations; it oversimplifies things, and it does not bring to light the complicated relationships many variables have. Logistic regression is another kind of linear algorithm. It represents a modification of linear regression since it applies linear regression to areas of classification, such as groups. Like linear regression, logistic regression is pretty easy to understand yet prone to oversimplification.

  1. Tree-Based Algorithms

Tree-based algorithms represent another kind of machine learning algorithm. There are three kinds of tree-based algorithms. One kind is a decision tree. It is used to match all applicable decision outcomes. It is not difficult to deploy, but it is not potent enough to handle complex data sets. Another kind is random forest. It averages out multiple decision trees. Random forests often create first-rate models, but they are not as easy to understand as decision trees; plus, they do not push out predictions very quickly. The final kind of tree-based algorithm is gradient boosting. It utilizes weak decisions trees for hard conditions. It performs exceptionally well; however, small changes can drastically alter its models. 

  1. Supervised Learning

Supervised learning is one branch of machine learning. It involves the use of labeled datasets for classification and prediction purposes. There are two elements of supervised learning. The first is regression; it is a technique used to determine continuous values. It yields an algorithm for forecasting, predictions, new insights, and process optimization. The second element is classification; it is employed to place novel observations into specific categories. Individuals will use a classification algorithm for image classification, fraud detection, customer retention, and diagnostics.

  1. Unsupervised Learning

Another branch of machine learning is unsupervised learning. It consists of learning algorithms that do not have labels. There are two notable components of unsupervised learning. Dimensionally reduction is one component; it facilitates big data visualization, meaningful compression, structure discovery, and feature elicitation. The other component is clustering. It yields targeted marketing, customer segmentation, and recommended systems.

  1. Reinforcement Learning

The last branch of machine learning is reinforcement learning. It prompts learning models to make a series of decisions based on trial and error. Just like in any reinforcement system, the models are rewarded for positive behaviors and punished for negative ones. People use reinforcement learning to cultivate real-time decisions, game AI, robot navigation, skill acquisition, and learning tasks. 

Machine learning has helped many people in several industries unlock the potential of their tools and systems. People will likely utilize machine learning in more fields as time goes by.

Publish Date: September 15, 2021 12:01 AM



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