Top 10 Machine Learning Algorithms

Top 10 Machine Learning Algorithms to Know

Top 10 Machine Learning Algorithms

Modern advancements in Artificial Intelligence (AI) are set to change our world for the better. These developments have largely been made possible due to technologies such as cloud sharing, data analytics, blockchain, and improved computing power.

These technologies have significantly improved machine learning, the main cause driver behind AI advancements.

Understanding Machine Learning

Machine learning is probably the most important component of developing Artificial Intelligence. The process of machine learning involves running repeated simulations on a computer, recording the results, and then running new tests based on the previous outcomes. The processor continues to make incremental improvements until it becomes advanced enough to represent a highly sophisticated level of AI.

The processor uses a number of algorithms to test hypothetical situations. These algorithms can be divided into three categories: supervised, unsupervised, and reinforcement algorithms.

  • Supervised Algorithms

This type of training algorithm requires both an input and an expected output for the data. The variables in the model are adjusted during the testing process to ensure that the outputs stay close to the expected goals.

  • Unsupervised Algorithms

These algorithms get inputs from the developers, but there are no specific outcomes expected during the testing phase. The algorithms may cluster the data sets together for different expectations.

  • Reinforcement Algorithms

These are the algorithms in which AI is expected to make a decision. The algorithms train themselves to improve after each decision, based on the success and/or failure of the output.

The most frequently used algorithms in AI development are covered below.

1) Linear Regression

This algorithm is somewhat simple compared to other algorithms. Linear Regression relies on using data points on a line that best fit the model to determine the solution. Drawing a straight line through plotted points helps solve the equation, y = ax + b.

In this equation, y is the dependant variable and x is the independent variable.  Calculus theory is applied to find the values for “a” and “b” that would make the best fit.

Linear regression can be further classified as simple linear regression or multiple linear regressions which make use of multiple independent variables to find the value of y.

2) Support Vector Machine (SVM)

This is a binary classification algorithm. It is plotted for a set of two points in N dimensional place, where SVM generates a (N-1) dimensional plane to separate a set of points into two groups.

As far as applicability is concerned, SVM is used to identify display advertising, image-based gender detection, and image classification into groups.

3) K-Nearest Neighbor (KNN)

This is a comparatively simple algorithm which is used to predict the nearest neighboring point for an element in a group. The value of K is quite important for the accuracy of the prediction. It makes use of the basic distance function, such as Euclidean, to determine the distance.

Despite its simple nature, the algorithm requires a very high computational power. This algorithm is very important in coordinate movement and finding a path to get from point A to point B.

4) Logistic Regression

Logistic Regression is a type of supervised algorithm where a specific output is expected. It is based on a predictive model where an algorithm is fed a large number of variables that could affect the outcome of an event.

For example, consider the possibility of a rain prediction. If all the factors that affect the chances of  rain are fed into the database, an algorithm should be able to predict the possibility of rain with 100 percent  certainty, give or take a small degree of error.

The algorithm uses a function to bunch values together to a particular range and creates an S curve. The possible range of predictions are 0, 1.

5) Decision Tree

The Decision Tree algorithm classifies population for a range of sets, based on some predefined properties. In most cases, this algorithm is used to classify similar items on the basis of some selected criteria.

For example, this algorithm would be used for farming to grade a crop of tomatoes on the basis of quality.

Another area where the algorithm could be applied is to determine the probability of a person applying for a credit card based on their marital status or age. When the system is fed data which shows the existing trends in applying for a credit card, the algorithm would be able to make a sound prediction.

6) K-Means Algorithm

This algorithm is used to determine solutions for clustering problems. The algorithm follows a procedure where it forms clusters which contain similar data points.

The value of K is fed to the database as an input. The neighboring data points to the value of K are combined to create a cluster. A new value of K is fed within the cluster, which forms further pockets of tightly knit groups.

The process is continued until clusters stop responding to new values of K fed to the system.

The algorithm has proved particularly useful in precision movement and robotics.

7) Random Forest

Random Forest is an advanced decision tree that is highly complex and enables algorithms to make sophisticated calculations. Each tree within the random forest works on the model of a decision tree algorithm.

Because of the complexity involved, random forest has a very high computational and hardware requirement. A single computation can take minutes and hours as the algorithm runs through every single nested tree within the forest.

Some developers believe that the process can be expedited with the help of blockchain, where multiple connected nodes go through separate trees, which can reduce the processing times.

8) Naïve Bayes

The Naïve Bayes algorithm is based on the Bayes Theorem of probability. One requirement of the theorem is that the features are independent of each other, which allows multiple computations to be run simultaneously.

For instance, if we are trying to predict the type of flower by simply holding and feeling its length and width, the Naïve Bayes approach can help us identify the correct solution, since both these characteristics of the flower are independent of each other.

This algorithm is generally used when there are classes in a problem.

9) Gradient Boosting Algorithm

This algorithm relies on using multiple weak algorithms to find a more powerful algorithm that can make accurate predictions. Instead of using one single estimator, the gradient boosting algorithm makes use of multiple estimators. This results in a faster and more robust central algorithm.

The gradient booster algorithm either applies linear algorithms or tree algorithms, depending on the needs of the developer.

10) Dimensional Reduction Algorithm (DRA)

It may be difficult for some types of databases to handle variables. This is because data collection in systems takes placed at a very detailed level due to the existence of more resources and data than necessary for computation. The data could become overwhelming for the algorithm to process, and most of it may not even be necessary to make a decision about a given problem.

There can be such a thing as too much data. DRA offers a solution to the problem of excess data.

DRA relies on using other algorithms, such as Random Forest and Decision tree, to quickly sift through the data and eliminate datasets that are not needed, focusing solely on datasets that are useful for finding a solution.

Conclusion

Testing hypothetical scenarios is at the core of what machine learning professionals do, from business analysts to information architects to developers. These ten algorithms will become extremely familiar and useful to anyone in machine learning, but they aren’t the only algorithms to master. Simplilearn’s Machine Learning Certification Course covers 15 common machine learning algorithms within its introductory lesson, in addition to lessons on supervised and unsupervised learning.

The best way to understand when to use these algorithms is simply by experience. Having these on hand to refer to is a start, but finding real-life applications for them is the best way to ingrain how they work, when to use them, and why.

By Ronald van Loon

Ronald van Loon

Ronald has been recognized as one of the top 10 Global Big Data, IoT, Data Science, Predictive Analytics, Business Intelligence Influencer by Onalytica, Data Science Central, Klout, Dataconomy, is author for leading Big Data sites like The Economist, Datafloq and Data Science Central.

Ronald has recently joined the CloudTweaks syndication influencer program. You will now be able to read many of Ronald's syndicated articles here.

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