Top Machine Learning Algorithms You Must Know
Top Machine Learning Algorithms: What You Must Know
Machine learning is one of the most exciting technologies that exist today. It has been able to attract a large number of interested learners and industry professionals. Machine learning algorithms allow a computer to learn from data without having to be manually programmed. In traditional computing, the user provides input and commands which the computer will execute to produce output. However, in applications where writing a manual program is not feasible for a wide range of situations, machines are taught to learn from incoming data and make its decision.
Over the past few decades, machine learning algorithms have found applications in a variety of fields from healthcare and finance to astronomy and warfare. One of the major reasons is the accelerated development of processor manufacturing technologies. Since machine learning algorithms tend to work with enormous amounts of data, it requires high computing power. Furthermore, cloud service providers like Amazon, Google, and Microsoft have made access to powerful machines easier.
There are four major categories of machine learning algorithms. We will learn about the top algorithms in each of the major types.
Supervised Learning Algorithms
In supervised learning, both the samples and their corresponding labels are passed to the machine learning algorithms. The algorithm learns from the training data and develops a model that is used to predict the results of unlabelled samples. Classification and regression are the two major types of supervised learning algorithms.
The top machine learning algorithms for supervised learning are:
Linear Regression
Linear regression is one of the most basic machine learning algorithms which you will typically learn in the first place. In this case, you will provide samples of data containing an independent variable and a dependent variable. Then, you will fit a function (typically a straight line) which will describe the relationship between those variables at best.
Let us consider that your data shows the number of flights per year over the past few decades. With linear regression, you can fit a model that can be used to predict the number of flights in the future.
The regression model is defined by the equation of a straight line
y = ax + b
Here, y is the dependent variable and x is the independent variable.
The values of a and b come from minimizing the defined cost function, which is typically the sum of squared errors. Here, the error is the difference between your input data points and the data points defined by the model.
Logistic Regression
It is one of the most basic machine learning algorithms used for classification. It is used to classify a data sample into two or more categories. If the number of categories is 2, it is called binary logistic regression whereas if there are more than 2 categories, it is multinominal logistic regression. This algorithm makes use of what is called an activation function which is used to map obtained values as probabilities. These obtained probabilities define the category of the input sample.
Consider a model that classifies whether an email is a spam or not. A feature vector keeps the information of the sender address, and frequency words like ‘discount’, ‘offer’, etc. You can use this to train a logistic regression model which will produce different values for spam and non-spam emails. The algorithm maps these values to different probabilities which then defines the category.
Decision Trees
Decision trees are also one of the most commonly used machine learning algorithms for both classification and regression. It consists of a tree-like structure formed by splitting the original set into multiple subsets. The flow advances from the top of the tree to the bottom node through these subsets where there is a test of a property. Classification is done based on the results obtained from each property test. Similarly, in the case of regression, you make a prediction using information from the same test.
kNN (k- Nearest Neighbors)
This is also one of the supervised machine learning algorithms used for both classification and regression. This algorithm decides the class of a sample based on the information about its neighbours. Consider a classifier that categorizes the image of fruit into ripe and raw. First, it maps the sample fruit into a feature space and inspects the status of its neighbours. Since similar types of samples are likely to be located closely, Since similar types of samples are likely to be located closely, you can then obtain the class. Despite its versatility and higher accuracy, it is still computationally expensive.
Unsupervised Learning Algorithms
In unsupervised learning, the algorithms work with unlabelled data i.e. the samples do not come with corresponding results. Therefore, there is no need for prediction. Clustering, or the segmentation of samples into different groups, is a type of unsupervised learning.
The most common machine learning algorithms used for unsupervised learning are:
k-Means Clustering
The major objective of k-means is to segment unlabelled samples into a number of groups or clusters (the number is defined by k). Consider a customer segmentation application. The application will take into account the purchase history, types of goods, and purchase amounts and map this information into a feature space. Similar types of customers will yield information that is likely to be closer in this feature space. k-Means will form a boundary between a cluster of data points that separate different types of customers.
Dimensionality Reduction Algorithms
Most of the machine learning algorithms typically work with large amounts of data. However, sometimes, the dimensions can be too hard to handle. This is true when you should consider a lot of features while developing models. Not all of these features are significant enough to be considered. The purpose of dimensionality reduction is to leave out insignificant features to increase the speed and efficiency of learning. Principle Component Analysis is a popular algorithm used for this purpose.
Reinforcement Learning Algorithms
Reinforcement learning algorithms are based on taking actions to maximize rewards in a particular situation. In contrast to supervised learning where the answer is known already, the agents learn from their experience and improve continuously. The application areas of reinforcement learning include robotics, industrial automation, and business management.
Conclusion
In this blog, we have learned the top machine learning algorithms that you should know and where they are applicable.
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