If you’re an AI professional or aspire to be one, one thing you must be aware of is: machine learning algorithms are your closest aid and ally. These algorithms can also be annoying. Given that there is a multitude of algorithms.
The knowledge of algorithms is essential to be an effective AI engineer, data scientist, and machine learning engineer. To give you a gist of how these algorithms work, let’s get down to know these algorithms.
Machine learning algorithms are categorized on the following basis:
- By learning method: An algorithm models a problem based on its interaction with data. The way an algorithm models the problem is used to group them under one category.
There are limited ways in which an algorithm can learn. This classification of algorithms is helpful because it aligns your thought process, so you can easily know the algorithm to use and where arrive at the right algorithm without much work. The classification allows you to think about the roles of the input data and the model generation preparation process, making the process easier for AI professionals.
- By similarity: A few algorithms are similar in the ways they work or function. So we group them together.
Grouping algorithms by learning method
On the basis of the learning method, algorithms can be broadly classified as – supervised or unsupervised learning algorithms.
- Supervised learning
In layman terms, algorithms learn under the supervision of a ready model. Input data, which is also called training data, as a result, or prediction. An algorithm tries to predict a similar result. If the prediction is wrong, it is corrected. The model continues to do so until it achieves the desired level of accuracy on the training data.
Classification and regression problems typically require supervised machine learning algorithms. Logistic regression and Back Propagation Neural Network are examples of supervised learning machine learning algorithms.
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