Machine learning stands out as a prominent subset within the realm of artificial intelligence, experiencing notable growth within the sphere of digital marketing.
And, of course, it is the one with the most significant number of people interested in creating their own machine-learning model.
If you are one of those people, you have to take into account that it is extremely important to know which machine learning algorithms are most frequently repeated when solving problems.
So, we took the time to investigate the 10 most used machine learning algorithms for Digital Marketing so you can create your own machine learning model. Let us begin!
1. Instance-Based Algorithms
They are learning models that act based on the resolution of decision problems through instances or training samples that are necessary or important for the model to be developed.
So, when you try to classify a new element, all those similar enough to use their classification and name or organize the new element are extracted.
These types of algorithms are also known as memory-based learning, and they basically create a model from a predetermined database.
In addition, they add new data in order to compare how similar they are with the samples that already exist in order to make a match and make a better prediction.
As extra information, we tell you that the most used instance-based algorithms in this group are k-Nearest Neighbor (kNN) and the Self-Organizing Map.
2. Dimension Reduction Algorithms
The main objective of this set of algorithms is to take advantage of the structure that exists in an unsupervised manner in order to simplify the data in order to compress it.
But what are these algorithms for? Simple! To much better visualize the data and to synthesize a set of variables for the use of a supervised algorithm.
In addition, it gives us the facility to graph those models that are very complex and that initially had multiple characteristics.
The most used dimension reduction algorithms when performing Machine Learning are t-SNE and Principal Component Analysis (PCA).
3. Decision Tree Algorithms
This is one of the most named and, therefore, most used groups of algorithms.
They are responsible for adjusting decision-making based on the fundamental and current values of the prominent data attributes.
How do they work? Well, they look for the best tree to balance the possibility of occurrence and define its importance in each branch and each leaf; in this way, they manage to catalog a result.
If you are wondering what they are used for, more than anything, to classify information, structure the paths that have been taken, and measure their probability of occurrence to improve their precision in each decision.
Classification and regression trees (CART) and conditional decision trees stand out among these algorithms.
4. Regression Algorithms
Regression algorithms are those that are responsible for modeling the relationship between different variables.
Using an error measure in a repetitive process to be able to make much more accurate predictions.
These types of algorithms are often used much more when performing statistical analysis.
A clear example of these algorithms could be predicting how many people suffer from hypertension based on common characteristics such as how much fat they consume and whether they have a healthy diet or not, among others.
If you are going to use these algorithms, try to use the most used ones, such as the linear regression algorithm and the logistic regression algorithm.
5. Clustering Algorithms or Grouping Algorithms
This type of algorithm belongs to the branch of unsupervised learning and is usually used to (as its name suggests) group and separate existing data from those we do not know.
This is done in order to find a relationship between the data and create middle points to differentiate the groups and discover what characteristics they have in common.
The most used algorithms in this group are k-means, k-medians, and hierarchical clustering.
6. Neural Network Algorithms
These types of algorithms are inspired by our neural network, thus developing deep learning, the autonomy of machine learning.
These algorithms are responsible for detecting patterns by imitating behavior and the interconnection of neurons.
This is in order to find a non-linear solution to various types of complex problems.
Generally, they are used to solve classification and regression problems. However, they have such a broad potential that they can solve a variety of issues.
It is worth mentioning that when the technology was not yet highly developed in previous years, this algorithm was very limited and required a lot of memory and processing capacity.
But today, thanks to technological advances, it has regained the strength to develop very complex processes.
Among the most prominent neural network algorithms in machine learning, you will find basic and classic networks such as xor gate, perceptron, multi-levered perceptron (MLP), and back-propagation.
7. Deep Learning Algorithms
These algorithms are the evolution of the artificial neural networks we discussed in the previous point, but like all evolution, they carry out different, although very similar, processes.
These algorithms convert large amounts of data into interconnected neural networks through a series of layers so that they can process and execute calculations in parallel.
You have the choice to select algorithms like convolutional neural networks or long short-term memory neural networks.
8. Bayesian Algorithms
These algorithms take advantage of Bayes’ probability theorems to solve classification and regression problems.
The prevalent algorithms in machine learning include Gaussian Naive Bayes, Bayesian Networks, and Multinomial Naive Bayes.
9. Natural Language Processing (NLP)
Natural Language Processing aims to enable a machine to understand human language, both written and oral, through a set of algorithms.
In addition to being used in machine learning to learn and understand the consumer to create chatbots or assistants like Siri, it can also be used in sentiment analysis on social networks.
10. Algorithms Based on Centroids
These types of algorithms belong to the unsupervised learning algorithm, and their main objective is to calculate the midpoint of the elements in order to minimize distances.
For example, there is a music giant that uses many of these algorithms.
Yes, Spotify uses machine learning algorithms to know what, how, when, and where to play the music you like.
Spotify, the foremost on-demand music platform globally, harnesses big data and machine learning to propel its business triumphs.
With over 100 million users, this digital music giant has dedicated considerable efforts this year to enhancing its service and technological prowess, facilitated by multiple acquisitions.
Data: Powerful Byproduct of Streaming Music
Streaming music yields a wealth of data, a valuable byproduct of its widespread consumption.
A trove of insights emerges with millions of listeners engaged round the clock. This information encompasses song preferences, geographical listening patterns, and the devices utilized for access.
Spotify exemplifies a data-centric approach across its operations, leveraging insights for decision-making.
Continuously accumulating data, the platform employs it to refine algorithms and train machines for music analysis.
A prime illustration is Spotify’s “weekly discovery” feature, which captured 40 million users within its inaugural year.
Each individual receives a bespoke playlist weekly, comprising unheard tracks tailored to their tastes.
Prompting reflection on Google Analytics for musicians
Initially available as a web version, the mobile app now grants musicians access to tour bus details and geo-streaming insights. Such information proves invaluable for efficient trip planning.
Furthermore, artists wield more significant influence over their Spotify presence, with options like “artist choice” and the ability to update bios and share playlists.
This reflects Spotify’s ongoing efforts to empower artists and foster trust. Another initiative, “Fans First,” leverages data to identify an artist’s most dedicated fans and offer them exclusive perks.
Spotify’s commitment to enhancement is evident through its acquisitions, like Niland, its fourth in 2017. Utilizing API-driven products and machine learning, Spotify aims to refine search and recommendations.
Moreover, its acquisition of blockchain startup media chain labs underscores efforts to streamline artist-licensing agreements.
Considering these advancements, how do you select the suitable algorithm for your project?
Initially, establish your goal with machine learning, followed by assessing the available data for achieving it.
Once these two aspects are clarified and considering the algorithms discussed earlier, you can select the most suitable one for your requirements.
