Technology is growing beyond our wildest imagination. There are cars that drive themselves nowadays.
And in the centre of it all is artificial intelligence (AI) and machine learning (ML). Your imagination only limits the scaling options provided by artificial intelligence. But to choose the machine learning model and algorithm is the key to unlocking your business' full potential.
There are various algorithms to choose from. There is no "universal algorithm" that can be used for any business; it solely depends on your business and its plan.
Here is a beginner’s guide on the different ML algorithms to help you build a successful business by leveraging the right algorithm.
Table of Contents
Machine learning algorithms receive and analyse data to predict outputs within an acceptable range. As new data is introduced, they develop ‘intelligence’ and improve performance with this data.There are mainly four machine learning algorithms:
- Unsupervised, and
1. Supervised learning
Like the name suggests this machine learning model requires human assistance to provide the data and algorithm updates.
Based on the historical data provided, predictions are made from previous examples or a set of examples.
For example, the historical data for sales can be updated into the system, and it will generate future prices. This is just one of the many operations. You can use an algorithm to optimise the training data to map input and output variables.
There are three types of data analysing in supervised learning.
- Classification: Categorical variable is predicted through this method. The images or data will be categorised into their designated areas through classification. There are two subtypes - binary classification (data with two labels) and multi-class classification (more than two types of labels).
- Regression: There will be instances where the data predicted will be continuous values, and there you will see a regression algorithm.
- Forecasting: This is the most common data analysing trend, where past and present data are used to make predictions. The target for the coming years can be calculated with forecasting.
Supervised learning has a disadvantage, and the dataset has to be hand labelled manually. When dealing with large volumes of data, this becomes a costly process as the data has to be handled by a machine learning engineer or a data scientist.
2. Semi-supervised learning
In this machine learning model, the algorithm works with unlabelled data. Limiting the labelling part of the data can increase the efficiency of reinforcement learning. Even though the labelling part of the learning process is reduced still, human assistance is necessary, and that is why it is known as semi-supervised learning.
3. Unsupervised learning
Using unlabelled data to discover the intrinsic patterns that underlie the data, such as clustering structure, dimension reduction or a sparse tree and graph.
- Clustering: It groups data into clusters and separates them into several groups. These groups can be analysed further to create intrinsic patterns that help the users ultimately.
- Dimension reduction: In many conditions, the raw data has very high dimensional features, and some features are irrelevant to the task. Removing this unnecessary data helps to refine the true latent relationship.
4. Reinforcement learning
There is no training data as a reference in reinforcement learning. The reinforcement agent decides what to do to perform the task. It learns from experience and improves the decision-making process.
Choosing the algorithm combines business need, specification, experimentation and time consumed. Even the experts cannot predict a model before experimenting with the algorithms.
When looking purely from a technical point of view, data size, quality and diversity are some of the main factors while choosing the right algorithm. There are some additional factors like accuracy, ease of use and training time.
Beginners tend to favour algorithms that are easy to use and have speedy results. This method is good as the first step, but as the business progresses, accuracy takes precedence. Strengthening your understanding of the data points helps you use more sophisticated algorithms to increase efficiency.
Machine learning algorithms are based on extensive training methods, so the accuracy of the algorithms increases according to the training sets.
- Naïve Bayes Classifier Algorithm (Supervised Learning - Classification)
A Naïve Bayes’ classifier is simple but can outperform many sophisticated classification methods. This algorithm classifies every value as independent of any other value based on the Bayes’ theorem. Using probability, it analyses the data and classifies them based on a given set of features.
- Support Vector Machine Algorithm (Supervised Learning - Classification)
Support Vector Machine algorithms analyse data used for classification and regression analysis. This is done by providing a set of training examples. The training data helps the algorithm filter data into categories and then works to build a model that assigns new values to each category.
- Logistic Regression (Supervised learning – Classification)
Logistic regression is used to cover a binary dependent variable, 0 and 1 represents the outcome.
- Linear Regression (Supervised Learning/Regression)
Linear regression allows us to understand the relationship between two variables. This is the basic type of regression.
- Decision Trees (Supervised Learning – Classification/Regression)
A decision tree algorithm is a tree structure that illustrates every possible outcome to a solution. Each node within the tree represents a test on a specific variable and the branches depict the outcomes.
- Random Forests (Supervised Learning – Classification/Regression)
Random forests or random decision forests are a combination of multiple algorithms to generate the best possible result. The algorithm starts with decision tree modelling and input is entered at the top. As the data moves down the tree, it is classified into smaller sets based on specific variables.
- Nearest Neighbours (Supervised Learning)
The Nearest Neighbours algorithm estimates how likely a data point is to be classified into a particular group. By analysing the features of the data points it can classify it into groups.
- K Means Clustering Algorithm (Unsupervised Learning - Clustering)
K means clustering algorithm is used to categorise unlabelled data. This helps to find groups within groups, and the number of groups is determined by the variable K. It assigns data points to one of the groups based on the features provided.
- Artificial Neural Networks (Reinforcement Learning)
An artificial neural network (ANN) comprises ‘units’ arranged in a series of interconnected layers. A large number of processing elements work in unison to solve problems. ANN also uses learn by example or experience which helps in the modelling of non-linear relationships in high dimensional data.
Deciding on a machine learning algorithm is important for building a successful business. As the future is being built on Artificial Intelligence and machine learning perspectives, the ML algorithm you choose will define your company’s trajectory.
You’ve now understood the different algorithms and how each can contribute to your company’s success. Even with this overview, seeking expert opinion is the best possible way to go.
Finding the right expert among the ‘so-called experts’ online is a gruesome and confusing process, we did a lot of research and we know the struggle. Here is a brief of insights from our researches to help you find the best experts.