No discussion of machine learning would be complete without a section devoted to Machine Learning Algorithms. Algorithms are a set of instructions for a computer on how to interact with, manipulate and transform data.
What Are Machine Learning Algorithms?
An algorithm can be as simple as a technique to add a column of numbers or as complex like identifying someone’s face in an image. To make an algorithm work, it must be composed like a program that computers can understand. Machine learning algorithms are most often written in one of the following languages: Java, Python or R.
Each of these languages includes machine learning libraries that support various machine learning algorithms. Also, these languages have active user communities that regularly contribute to the code and discuss ideas, challenges and approaches to trade issues.
The Role of Machine Learning Algorithms
Machine learning algorithms are different from other algorithms. With most algorithms, a programmer begins by entering the algorithm. However, with machine learning, the process is reversed. With machine learning, the data itself creates the model.
The more data that is added to the algorithm, the more sophisticated the algorithm becomes. As the machine learning algorithm is exposed to more and more data, it can create a more and more precise algorithm.
Types of Machine Learning Algorithms
Selecting the right algorithm is both science and art. Two data scientists tasked with solving the same business challenge can choose different algorithms to address the same problem. However, understanding different classes of machine learning algorithms helps data scientists identify the best types of algorithms. The algorithms below give you a brief overview of the main types of machine learning algorithms:
Bayesian algorithms allow data scientists to encode past beliefs about what models should look like, regardless of the state of the data. With so much focus on the data defining the model, you might wonder why people would be interested in Bayesian algorithms. These algorithms are especially useful when you don’t have huge amounts of data to train a model with confidence.
Clustering is a fairly simple technique to understand – objects with similar parameters are grouped. All objects in a cluster are more similar to each other than objects in other clusters. Clustering is a type of unsupervised learning because the data is not labelled. The algorithm interprets the parameters that make up each element, then groups them accordingly.
Decision tree algorithms use a branching structure to illustrate the results of a decision. Decision trees can be used to map the possible outcomes of a decision. Each node in a decision tree represents a possible outcome. Percentages are assigned to nodes based on the likelihood of the outcome occurring.
Regression algorithms can quantify the strength of the correlation between variables in a data set. In addition, regression analysis can be useful in predicting future values of data based on historical values. However, it is important to remember that regression analysis assumes that the correlation is related to causation. If you don’t understand the context around the data, regression analysis can lead you to inaccurate predictions.
A neural network is inspired by the way a human brain approaches problems and uses layers of interconnected units to learn and infer relationships based on observed data. Models of neural networks can adapt and learn to measure as data changes. Neural networks are often used when data is unlabeled or unstructured. One of the main use cases of neural networks in computer vision.
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