Over the years, machine learning has become incredibly prominent in our everyday work, particularly in the business industry.
Machine learning can help computer networks operate medical procedures, play online games, and become more intelligent.
Hence, multiple machine learning algorithms have been proposed to deal with real-world compound problems in today’s interactive times. The ten algorithms discussed here in this article are fully automated and identity, which means they improve significantly with the addition of more data and human interference.
Before we proceed to 10 popular machine learning algorithms, let us first understand the machine learning algorithm.
- 1 What is Machine Learning Algorithm?
- 2 Types of Machine Learning
- 3 10 Popular Machine Learning Algorithms
- 4 Final Thoughts
What is Machine Learning Algorithm?
Machine learning algorithms are a mathematical figure-mapping method used to understand or discover fundamental patterns in data. It refers to a group of computational algorithms which can conduct pattern recognition, identification, and forecasting data by understanding from data.
No doubt, machine learning has become more prominent through the years. In addition, big data is rapidly growing in the technology business, wherein machine learning effectively makes predictions or generates suggestions based on large amounts of data.
A combination of factors has led to a massive machine learning boom, including the easy availability of data and faster computational processing. Cheaper data storage has also helped. As a result, businesses can now implement machine learning in their operations.
Types of Machine Learning
With the impact of ML in our daily lives, machine learning algorithms are categorized into three, supervised, unsupervised, and reinforcement:
- Supervised Learning
If a dataset is assigned three different labels, a data mining algorithm takes this into account and detects any patterns among the values. A significant total of labeled data is needed to perform this algorithm. Machine learning algorithms may understand and categorize unlabeled data by combining these two techniques.
- Unsupervised Learning
It is used when determining implicit connections in an unlabeled dataset is challenging. They categorize the data into groups. Therefore, whatever input data is automatically available for analysis.
As it analyzes additional data, its capacity to make decisions based on that data grows and refreshes.
- Reinforcement Learning
Reinforcement is often used to find the best solution for a particular situation. As a result, the algorithm selects what to do next based on its present condition and what it believes would maximize its benefit in the long run.
Moreover, it focuses on structured learning processes wherein a machine learning algorithm is given a set of actions, variables, and end values.
10 Popular Machine Learning Algorithms
Several machine learning algorithms are developed during this challenging time to transmit the problems.
Now that we understand what an ML algorithm is, let’s look at some of the top algorithms available.
Linear regression is one of the statistics and well-known algorithms. It displays the impact of changing the independent variable on the dependent variable. So, the independent variable examined the analytical variable while the dependent variable was introduced as the interest component.
By putting independent and dependent variables into a line, a connection between them is created. The regression line is shown as Y = a * X + b, a linear equation.
In this equation:
- a – Slope
- Y – Dependent Variable
- X – Independent variable
- b – Intercept
A and b are the coefficients that are determined by reducing the sum of squared length between the regression line and data points.
Logistic regression is an effective statistical tool for modeling a binomial output in the presence of one or more informative factors. When a logistic function is used to analyze feasibilities, it evaluates the relationship between a dependent variable and one or more independent variables.
The Logistic Regression Algorithm is suitable for binary classification since it works with discrete variables. An event is categorized as one if it occurs or 0 if it does not.
The frequency of a certain event happening is evaluated using predictor factors provided. The logistic function is required to change the output predictions compared to linear regression.
The Decision Tree algorithm is a learning technique used for problem classification. Also, it is one of the most widely used machine learning algorithms today. The decision tree categorizes either continuous or categorical changes in the dependent variable.
A decision tree algorithm is used in the banking sector to identify loan applicants according to their risk of failing on monthly payments.
4Naive Bayes Algorithm
Naive Bayes is a basic yet powerful predictive modeling algorithm. This technique utilizes the Bayes Theorem of Probability to distribute the enhancement of quality to a group from one of the given categories.
In addition, Naive Bayes refers to two forms of probabilities: the probability of each class and the conditional probability. These two probabilities can be determined directly using training data, and Bayes’ theorem can also predict new data using the probability structure.
One of the most prominent ML algorithms is the K-Means Clustering Algorithm. It is utilized for clustering, which helps group various comparable data sets. Images and videos, as well as text documents and online sites, might be included.
K-Means Algorithm is often used in Google image search, search engines (Bing and Yahoo), data libraries, and Microsoft Machine Learning Studio.
The K Means Clustering Algorithm works on a data set with K clusters. The input data is distributed between K clusters in the output.
6K-Nearest Neighbor Algorithm
K-Nearest Neighbors is a machine learning algorithm used for classification and regression problems, like K-Means. It is mainly used in statistical estimates and pattern recognition.
It’s a simple algorithm that stores all the rules and explores different situations depending on the number of votes from its k neighbors. The case is assigned to the group and has the most similarity. The measurements have been carried out through a distance function.
In short, the best way to understand this algorithm is to use any situations from your everyday life.
In a Random Forest, the number of decision trees is stated. Each tree is classified, and the class receives three votes to identify new items based on their features. The forest selects the classification with the highest votes.
Random Forest can be seen in the following ways:
- To assist banks in predicting high-risk loan applicants
- To detect the failure or breakdown of a structural element
- To determine if a patient is prone to have a chronic condition.
- To complete the average number of social media clicks for the given article.
A decision tree that includes a range of data enables a more exact identification. It is also extraordinary since they help you to keep its productivity. It’s also compelling towards outliers and requires only a few lines of code to create.
8Gradient Boosting and AdaBoost
The AdaBoost and Gradient Boosting algorithms work with a lot of data to make precise predictions. These methods are frequently used in data science contests such as Hackathons.
Gradient Boosting is a method of learning that develops a weak or average classifier into an active predictor. It would be performed by developing a model using training data and afterward building the new model to fix the problems from the first model. Models are also added until the data set is accurately predicted.
On the other hand, AdaBoost is an effective boosting method for binary classification. It is the first step in improving learning and comprehension. Most recent boosting techniques are built on AdaBoost, particularly stochastic gradient boosting machines.
9Support Vector Algorithm
Support Vector Machines are the most used and popular ML algorithms today. It is a classification algorithm procedure wherein raw data is shown as dots in an n-dimensional landscape. Every character’s number may be allocated to a single place, making data identification straightforward. Classifier lines help split data and display it on a graph.
SVM is most commonly used in stock market prediction and risk evaluation. It’s also used to evaluate the stock’s performance compared to other equities in the same category. It benefits businesses in choosing where they want to invest their money.
10Dimensionality Reduction Algorithms
The process of reducing the number of characteristics in a data collection is referred to as dimensionality reduction. Often, there are variables to deal with in machine learning tasks such as regression. These factors are also referred to as features. It becomes more challenging to express it as more features are added.
This strategy is often used to move data from a high-dimensional feature space to a low-dimensional feature space. Moreover, it is important since no relevant data attributes are lost throughout the transformation.
Dimensionality reduction is also frequently utilized in visual analytics to better understand and evaluate machine learning and deep learning data. These approaches make tasks more manageable.
If you want to continue your knowledge in machine learning, start now! This field is growing, and you will soon reach machine learning techniques sooner or later. The faster you are, the faster you can handle complex work challenges.
However, when selecting the best machine learning algorithm for your daily needs, there are many things to consider. Check for what suits you according to the problems and data you have to solve.
There you have it! I hope this blog will guide you in determining machine learning algorithms.