Top 10 Machine Learning Algorithms in 2024

A machine learning algorithm is a part of Artificial Intelligence where the AI does its work like predicting future results based on the given data. There are various types of machine learning algorithms that help computers in learning and become smarter and more human-like. We are in a time of constant technological advancement. By using various machine learning algorithms, we can make predictions about future events more accurately and with ease.

There are various machine learning applications that have come out to be really helpful. In the last five years, data scientists have created data-crunching machines by using cutting-edge methodologies. Various machine learning algorithms have been developed that help in resolving challenging situations in the real world. Let’s take a closer look at the different algorithms in machine learning and how they are categorized.

Types of Machine Learning Algorithms

Here are the four types of machine learning:

• Supervised learning
• Unsupervised learning
• Semi-supervised learning
• Reinforcement learning

Further, the above-mentioned ML algorithms are classified into many more types on the basis of their functionality and the specific use of the algorithms.

List of Top 10 Machine Learning Algorithms

Let us briefly look over all these Machine Learning (ML) algorithms.

Linear Regression

Let’s take a situation where we need to arrange the wooden logs of different sizes in ascending order of weight and we are not allowed to weigh the wooden logs. The only option for arranging these wooden logs according to their weight is to place them in the ascending order of their size. It is assumed that the log smaller in size will also have less weight. So, the decisions are made on the basis of the visual parameters of the problem. 

The same concept is used in the linear regression algorithm of machine learning. There are two variables in the linear regression algorithm. These are the independent variable and the dependent variable. A relationship is established between these variables and they are set to a line, known as the “regression line”. The equation of the regression line is Y = a*X + b.

where,

Y is the dependent variable

a is the slope 

X is the independent variable

b is the intercept 

Logistic Regression

When there are a set of independent variables (generally binary values like 1s and 0s) and we need to estimate these discrete values, we use logistic regression in the machine learning algorithm. Using the logistic regression algorithm, the probability of any specific event can be calculated. This is done by setting the discrete values into a logit function, which is why this algorithm is also known as the logit regression.

The various methods that we use to improve the logistic regression models are as follows:

• Include interaction terms.
• Eliminate features.
• Regularize techniques.
• Use a non-linear model.

Decision Tree

The decision tree is one of the most popular machine learning algorithms. It is a type of supervised learning algorithm used to categorize given problems. This algorithm is used to categorize the given problems. The problems can be further divided on the basis of the category of the problem as well as the continuous dependent variables. This algorithm divides the given problem into two sets. These sets are made on the basis of the variables or most important attributes. This is known as the decision tree algorithm.

SVM Algorithm (Support Vector Machine algorithm)

The support vector machine algorithm is a type of machine learning algorithm in which all the raw data are considered as a point. All of these points are plotted in space. This plot is of n-dimension as all the features of a particular problem are treated as a separate dimension. So, the number of dimensions is equal to the number of features of a problem. These plotted points are then tied to a particular coordinate. This makes it easy to classify the data. In the plotting graph, there exists a line known as the “classifiers” used to split the data on a graph.

KNN (K-Nearest Neighbours) Algorithm

The KNN algorithm is a machine learning algorithm that can be used for classification and regression problems. However, in the Data Science industry, the KNN algorithm is mostly used for classification problems. In this algorithm, all the present cases are stored. After that, the new cases are determined by accepting the major votes of its K-Neighbours. Then, the case is submitted to the class which has the most common attribute among them. The most common attribute is measured by a function known as the “Distance function”.

There are some keys that we need to consider while selecting the K Neighbour algorithm which are as follows:

• KNN is an expensive algorithm computationally.
• The variable present in this algorithm needs to be normalized.
• Data needs to be pre-processed.

K-Means Algorithm

The K-Means algorithm is a type of unsupervised machine learning algorithm that is used to solve the problems that exist in a group or cluster. In this approach, the data sets are separated into groups or clusters so that all the data types are of different categories. The data points inside a cluster should be homogenous and heterogenous to data points of other clusters.

The K-Means forms cluster in the following ways:

• This algorithm forms the cluster by picking a definite number of points in each of the clusters which are called centroids.
• All the data points form a cluster with the centroid that is closest to them. 
• Now, new centroids form based on the existing member of the cluster.
• As the new centroid forms, the distance for each point is determined. The measuring of the distance keeps continuing until the centroid does not change.

Naive Bayes Algorithm

The Naive Bayes is a type of supervised machine learning algorithm. As the name suggests, this algorithm is based on Bayes’ Theorem and it is mainly used for problems of classification. This algorithm is a probabilistic classifier because further predictions are made based on the probability of the object. 

It is a very simple and effective machine learning algorithm that helps in building models that are quickly responsive and fast. The various examples of the Naive Bayes algorithm are spam filtration, classifying articles, and sentimental analysis. Now, let us have a brief look at what exactly is Bayes’ Theorem.

Bayes’ Theorem: This theory is mostly used in mathematics to solve problems related to probability. It is used to find the probability of a hypothesis with current and prior information. The formula for Bayes’ theory is as follows:

where, 

P(A|B) = Posterior probability 

P(B|A) = Likelihood probability

P(A) = Prior probability

P(B) = Marginal probability

Dimensionality Reduction Algorithm

In today’s era, where everything is going online and on a cloud, there are enormous amounts of data being transferred and processed in a fraction of a second. So, the challenge is to specify the patterns and variables in these large data sets, as this data contains a lot of information. The various examples of the dimensionality reduction algorithm are Factor Analysis, Random Forest, Decision Tree, and Missing Value.

Random Forest Algorithm

Random Forest is a type of supervised machine learning algorithm. This algorithm is used for classification problems as well as regression problems. In it, many classifiers are merged for finding a solution to a complex problem. This also increases the performance of a model.

After calculating the average, the Random Forest algorithm consists of many decision trees on various subsets of the provided large data set. This improves the accuracy and precision of the solution. This algorithm supports the concept of relying on many trees rather than making decisions based on one tree. The resultant output depends on majority vote prediction. If the number of trees is larger in the forest, it will lead to higher accuracy of the solution. Given below is a diagram that shows the workflow of the Random Forest algorithm:

Gradient Boosting Algorithm and AdaBoosting Algorithm

The Gradient boosting algorithm and AdaBoosting algorithm are used to make predictions when the data set is large and very high accuracy is needed in the result. Boosting is a collective learning approach that increases robustness by combining the predictive strength of multiple base estimators. In general, we can say that many weak predictors come together and form strong predictors. 

Why Should You Learn Machine Learning Algorithms?

Learning machine learning algorithms can provide many benefits, such as:

Increased Efficiency

By automating tasks that were traditionally done manually, machine learning algorithms can increase productivity and efficiency. They are capable of processing large datasets in a relatively short amount of time, which makes them a powerful tool for businesses looking to get ahead of their competition.

Cost Savings

Using machine learning algorithms instead of manual labor or services may result in significant cost savings over the long run. Additionally, these techniques don’t require advanced hardware infrastructure as they mostly involve running code on existing machines rather than purchasing expensive dedicated computers or servers specifically used for data analysis purposes only.

Better Salaries

The salaries of machine learning engineers have become just as competitive as those paid to venerated sports celebrities. Typically speaking, a recent college graduate entering this engineering field can expect an average salary of about 8 lakhs in their role, according to Glassdoor. Experienced professionals will experience much higher earning potential within 15-23 Lakhs per annum or more depending on applicable skill sets and base responsibilities associated with any given employer.

ML Relates to Data Science

Gaining competence in both machine learning and data science may take your career to incredible heights. The correlation between the two fields is close; while ML relies on DS, most organizations make use of a combined team of data scientists and machine learning engineers for their products. Analyzing large amounts of information often requires an understanding from multiple angles. This includes extracting value as well as gaining insights into it. This makes knowledge about both these areas invaluable for many businesses out there today.

Learning ML Brings More Career Opportunities

Machine learning is becoming an increasingly important part of business intelligence and has opened up many career opportunities. Companies like Netflix are offering large sums of money for improving ML algorithms in order to increase their profitability, showing just how significant it can be. With this increased demand for skilled ML professionals, studying machine learning gives people the chance to develop cutting-edge applications across various fields that involve AI technologies. This includes fields, such as security, recognition systems, or medicine. 

Conclusion

Machine learning algorithms are used to make future predictions based on present collected data. They are divided based on functionality and their specific use such as linear, non-linear, etc. The field of machine learning and data science is growing exponentially. If you are a data science enthusiast, it is advisable to keep an eye on the advancements in machine learning algorithms to cope with the current trends.