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:
1. 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.
2. By similarity: A few algorithms are similar in the ways they work or function. So we group them together.
We discuss each category in detail below. So let’s start.
Grouping algorithms by learning method
On the basis of the learning method, algorithms can be broadly classified as – supervised or unsupervised learning algorithms.
1. 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.
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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.
2. Unsupervised learning
The provided training data doesn’t have a label and no fixed result. The model is prepared by deducing structures in the input data. This is done by mathematical processes to reduce redundancy or organize data by similarity.
Clustering, dimensionality reduction, and association rule learning have commonly solved the problem using unsupervised ML algorithms. As an AI engineer, you will frequently arrive at situations where you will need to use such algorithms. Apriori and K-means algorithms are examples of unsupervised ML algorithms.
3. Semi-supervised learning
Semi-supervised learning combines both the above approaches, meaning the input data contains both labeled as well as non-labeled data.
The model learns to make predictions from the labeled data, but first, it must organize the data.
Classification and regression problems widely use unsupervised algorithms. Supervised and unsupervised machine learning algorithms are widely used, semi-supervised algorithms are used less frequently.
Grouping algorithms by similarity
We can group algorithms by the way they function. For instance, we can club a few algorithms under tree-based algorithms and neural –network methods. However, there are algorithms that can fit into multiple categories, so this isn’t as effective as it’s thought to be.
So let’s discuss the most common group of machine learning algorithms.
Regression is a process to model the relationship between variables. The process is refined using a measure of the error in the predictions made by the model.
The following are a few frequently and most widely used regression algorithms.
• Linear Regression
• Logistic Regression
• Stepwise Regression
• Multivariate Adaptive Regression Splines (MARS)
• Locally Estimated Scatterplot Smoothing (LOESS)
• Ordinary Least Squares Regression (OLSR)
2. Clustering algorithms
Clustering algorithms read the inherent structure in the data and then organize the data into groups of maximum commonality.
The following algorithms fall into this category.
• Expectation Maximisation (EM)
• Hierarchical Clustering
3. Decision Tree algorithms
Decision tree algorithms construct a model of decisions based on actual values of the attribute in the data.
Decision trees are perhaps the most accurate predictors. Also, these algorithms are fast. Decisions carry on in the form of a tree until a decision is made.
The following are a few popular decision tree algorithms that every AI professional should know.
• Chi-squared Automatic Interaction Detection (CHAID)
• Decision Stump
• Conditional Decision Trees
4. Deep learning algorithms
Deep learning algorithms are improved versions of artificial neural networks. These algorithms are concerned with building sophisticated neural networks. The large datasets can be images, text, documents, audio, and video.
The following are a few frequently used deep learning algorithms.
• Convolutional Neural Network (CNN)
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory Networks (LSTMs)
• Stacked Auto-Encoders
• Deep Boltzmann Machine (DBM)
• Deep Belief Networks (DBN)
In addition to the above group of algorithms, there are other groups like dimensionality reduction, ensemble algorithms, regularization, Bayesian, Association learning, etc, which can be categorized in other groups.