Coursera Learner working on a presentation with Coursera logo and
Coursera Learner working on a presentation with Coursera logo and

Our world is changing through technological advancements, and machine learning is at the forefront. It is one of the factors affecting how machines perform tasks. Through machine learning, we can perform various basic and complex daily tasks. The applications of machine learning include voice recognition, image screening, etc. This computational science field helps interpret and analyze the structure and pattern of the data to perform complex tasks. These complex tasks will often require decision-making, reasoning, and learning.

In simple words, you can insert numerous data into the algorithm of the system. The system will then analyze that data and identify the data’s pattern and structure to make decisions. Furthermore, the algorithm will use new results to improve future decision-making. Below you will find various types of the machine learning algorithm:

Types of the Machine Learning Algorithm by Learning Style

1.    Supervised Learning

With this learning algorithm method, you train your machine by providing the right answers for the data. That means all the data have accurate answers. In supervised learning, you need a professional or supervisor with high data science skills to perform the training tasks. You can predict the unseen data outcome by providing the machine with labeled data in the training process.

This learning algorithm is similar to learning institutions where a teacher provides you the knowledge about a subject. And you start to explore more about the subject on your own.

How Does the System Work

Let’s say you want to create an algorithm that tells you how long you will travel from your office to work. You need to provide your system with the following information:

  • Route
  • Traffic on that route
  • Time of the day
  • Weather condition

Once the system receives this information as the input, it can tell you how much time you will need to reach home. The input would be the data you provide or the system is receiving through a source you assign. Your system will calculate the information through the algorithm and provide you the output. So in supervised learning, the machine needs to collect data and conditions instantly and analyze the result.

2.    Unsupervised Learning

Unsupervised learning lacks the need for human intervention to provide references and data. The system will collect the data from unknown sources, analyze it, and generate the output. It is impossible to train this type of machine—these types of machines help discover unknown patterns and data structures. The result may be poor sometimes than supervised learning, but there is no way you can find the output’s accuracy.

How Does the System Work

As this type of machine learning does not require labeled data, the coded data and algorithm work together for the output. The purpose behind unsupervised learning is figuring out the hidden patterns and trends. For instance, if you run a factory that produces computer chips. You need to maintain various factors, such as temperature, humidity, and vibration, to avoid chip failures. However, you found out that more chips are failing. To find the reason and combination of factors that affect your production, you can use an unsupervised clustering algorithm.

3.    Semi-Supervised Learning

As the name implies, this type of machine learning is a mix of supervised and unsupervised machine learning. In this learning method, you provide labels of some data from the system while some data is not labeled. The main task of this learning system is to categorize the unlabeled data by analyzing the labeled data.

How Does the System Work

Since labeled data is expensive, Semi-supervised learning machines create labeled data from unlabeled data with minimum resources. Companies such as Google are using SSL to label their data. The system will analyze the labeled data pattern and use the same pattern to label the data that does not include any label. For instance, if you want the system to recognize birds around your house, you will add labeled birds’ pictures. Using this labeled data, the system learns about the birds’ features, recognizing them instantly. With new pictures that you add to the system, it will identify the birds through its algorithm.

Types of the Machine Learning Algorithm by Similarities

Find below various other types of the machine learning algorithm that are commonly used:

1.    Regression Algorithm

A regression algorithm is a type of supervised machine learning. This algorithm works according to the relationship between input and output and generates new data. For instance, if you want to know the house’s price, the system will predict its price depending on the features you provide. These features can include: how many bedrooms, what is the area, etc. The most popular regression algorithms are:

  • Ordinary Least Squares Regression (OLSR)
  • Linear Regression
  • Logistic Regression
  • Stepwise Regression
  • Multivariate Adaptive Regression Splines (MARS)
  • Locally Estimated Scatterplot Smoothing (LOESS)

2.    Memory-Based Algorithm

Other names for this machine learning system include instance-based system or lazy learning. This algorithm will compare every new problem to the system’s data through the learning process. For instance, if you want to find several emails based on any keyword or the sender’s name, a memory-based algorithm will search in your inbox and find all the emails that include that keyword or from the specific sender. The most popular memory-based algorithms are:

  • k-Nearest Neighbor (kNN)
  • Learning Vector Quantization (LVQ)
  • Self-Organizing Map (SOM)
  • Locally Weighted Learning (LWL)
  • Support Vector Machines (SVM)

3.    Regularization Algorithms

A regularization algorithm simplifies the data or problem to help reduce errors and overfitting. To make the machine more flexible, this algorithm reduces the risk of overfitting. This algorithm will make the machine learn rather than memorize the data to perform well with the unseen data. The most popular regularization algorithms are:

  • Ridge Regression
  • Least Absolute Shrinkage and Selection Operator (LASSO)
  • Elastic Net
  • Least-Angle Regression (LARS)
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4.    Artificial Neural Network Algorithms

This algorithm mimics the inner workings of the human brain. The brain contains various neural networks that transfer the data from input to output after processing it. Similarly, this same technique of input and output layers can help train machines. Artificial neural network algorithms also consist of a hidden network that calculates the result and transfers to the output.

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Machine learning is the future technology that will help in solving a lot of future problems and tasks. Data is the fuel of every industry. With machine learning, we can gather, understand, and find new trends and patterns to make better and informed decisions. The machine learning algorithm can open new doors in the field of AI and automation.


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