Machine learning is a branch of artificial intelligence that uses algorithms to learn from data. It’s a popular tool for businesses because it can be used to automatically make predictions or recommendations, without human intervention. In fact, a recent study reveals that 78% of top companies rate machine learning as an important revenue driver for 2023.
There are many different machine learning algorithms, and each has its own strengths and weaknesses. While machine learning is a powerful tool, it can be overwhelming to understand all the different machine learning algorithms.
This post will show you the top 15 machine learning algorithms and their uses. You don’t need to be a data scientist or have advanced coding skills to understand and use these machine learning algorithms.
Traditional software required explicitly programming a defined set of rules to get the desired output. Machine learning algorithms, on the other hand, automatically learn from data to improve their performance.
There are many different types of machine learning algorithms based on their use case, complexity, the type of data they can learn from, and more. Before we look at the top 15 ML algorithms, let’s first look at the categories they can be classified under:
Supervised machine learning algorithms are trained using labeled data, meaning that the algorithm knows the correct output for a given input variable in the training set. For example, you can use a supervised learning algorithm to predict whether an email is spam or not. The binary classification algorithm is first trained on a dataset of labeled emails (spam or not spam) and then makes predictions on new emails.
The business tools you use on a day-to-day basis generate a lot of labeled data that can be used to train supervised learning algorithms. For example, HubSpot’s Contact Management system creates a label for each contact (subscriber, lead, marketing qualified lead, opportunity, etc.) so that you can segment your contacts and personalize your marketing campaigns.
Unsupervised learning algorithms are trained using unlabeled data. The algorithm must learn from the data itself to find patterns or relationships. For example, you can use unsupervised learning to cluster customers by their behavior. This is useful for businesses because it can help you automatically segment customers without having to manually label them. Semi-supervised learning refers to the use of both labeled and unlabeled data.
Reinforcement learning algorithms are different from supervised and unsupervised learning algorithms because they learn by taking actions and receiving feedback. The goal is to maximize a reward, such as winning a game or completing a task. For example, Google DeepMind’s AlphaGo algorithm was trained using reinforcement learning to beat a professional Go player.
Now that we’ve covered the basics of machine learning algorithms, let’s look at the top 15 machine learning algorithms that you should know.
We all use decision trees every day without realizing it. Whenever you choose what to wear in the morning or what route to take to work, you’re using a decision tree.
Decision trees are supervised learning algorithms that can be used for both classification and regression tasks. The algorithm splits the data into different groups (called branches) based on certain conditions (called splitting rules). Each branch represents a possible decision, and the final decision is represented by a leaf node.
The chief benefit of this method is that it’s interpretable. The decision tree can be visualized, and the decisions made by the algorithm can be traced back to the root of the tree. This is helpful for businesses because it allows them to understand how the algorithm arrived at a particular decision.
However, decision trees are not always the most accurate predictive models. They are susceptible to overfitting, especially when used on small datasets.
Gradient boosted decision trees (GBDTs) are an improvement on traditional decision trees because they simplify the objective and reduce the number of iterations needed.
GBDTs work by sequentially adjusting the values of the coefficients, weights, or biases applied to each input variable. The gradient refers to the incremental adjustments being made, while boosting is a way to accelerate improvements in predictive accuracy.
Classification and regression trees (CARTs) are a type of decision tree. CARTs work by recursively partitioning the data into different groups. The algorithm starts at the root node and splits the data into two groups. It then repeats this process at each child node until the data is partitioned into leaves. The final nodes represent the predicted class label.
Unlike a regular decision tree, however, CARTs use Gini's impurity index to split a node into a sub-node. The Gini index measures how much every specification affects the resulting case.
Random forest is an ensemble learning algorithm that can be thought of as an improvement on decision trees. Rather than growing a single decision tree, the random forest algorithm grows multiple decision trees and combines them to create a single, more accurate model.
The individual decision trees in a random forest are grown using a different subset of the data. This process is repeated until all the data has been used to grow a tree. The final model is then created by combining the predictions of all the individual classifiers.
Random Forests are popular because they are accurate and scalable. However, like GBDTs, random forests are more difficult to interpret than decision trees.
K-means clustering is an unsupervised learning algorithm that can be used to segment data into groups (called “clusters”). The "k" in k-means clustering refers to the number of clusters that the algorithm will create.
The algorithm works by first randomly choosing "k" data points to be the centroids of the clusters. It then assigns each data point to the cluster that has the closest centroid. The algorithm then iteratively moves the centroids to the center of their respective clusters and reassigns data points accordingly. This process is repeated until the centroids no longer change.
Linear regression is perhaps the simplest and most widely used machine learning algorithm. One common example of linear regression is predicting housing prices.
The linear regression algorithm models the relationship between a dependent variable (housing prices) and one or more independent variables (size of the house, number of bedrooms, etc.). The algorithm then uses this model to make predictions on new data.
Linear regression is popular because it’s simple to understand and implement. It’s also computationally efficient and scalable. However, linear regression often fails for more complex, real-world datasets.
Neural networks are a type of machine learning algorithm that are conceptually modeled after the brain. Neural networks consist of a large number of interconnected processing nodes (called neurons) that can learn to recognize patterns of input data.
Neural networks are used for a variety of tasks, such as image recognition and classification, natural language processing, and time series prediction.
The "layers" in a neural network refer to the different levels of abstraction in the data. The input layer is the raw data, and each subsequent layer extracts increasingly complex features from the data. The final output layer produces the predicted class label or value.
Neural networks are popular because they can learn complex patterns in data. However, they are also computationally intensive and require a large amount of training data.
Deep learning refers to neural networks with many layers, using big data and computation to automatically extract features from data. Complex tasks like image recognition and natural language processing can be done with deep learning.
Deep learning is a newer, more advanced version of neural networks. The main difference is the number of layers in the network. Traditional neural networks have a few layers (e.g., input, hidden, and output), while deep learning networks have many layers (e.g., input, hidden1, hidden2, … , output).
The additional layers in deep learning networks allow them to learn more complex patterns in data. However, deep learning networks are also more computationally intensive and require more training data than traditional neural networks.
Support vector machines (SVMs) are a non-probabilistic learning algorithm, meaning that they don’t directly compute probabilities.
The SVM algorithm works by finding the best line (or hyperplane) that separates the data points into two groups. The line is then used to make predictions on new data points. Unlike linear regression, SVMs can be used for non-linear classification tasks.
SVMs are popular because they are accurate and scalable. They also have the ability to handle complex, non-linear classification tasks. However, SVMs can be difficult to interpret and tune.
K-nearest neighbors (KNN) is a non-parametric, lazy learning algorithm. Non-parametric means that the algorithm doesn’t make any assumptions about the data. Lazy means that the algorithm doesn’t learn a model until it’s necessary to make a prediction.
KNN works by finding the "K" closest data points to a new data point and using them to predict the label of the new data point. The "K" is a hyperparameter that can be tuned.
Not to be confused with K-means clustering, which is also a "K" based algorithm. K-means clustering is an unsupervised learning algorithm that groups data points into k clusters. KNN is a supervised learning algorithm that makes predictions through a process of similarity measurement.
Anomaly detection is a type of unsupervised learning algorithm that is used to identify outliers in a dataset. Outliers are data points that are significantly different from the rest of the data.
Anomaly detection is used for a variety of tasks, such as fraud detection, network intrusion detection, and mechanical fault detection.
There are many different anomaly detection algorithms, but they all share a common goal: to identify data points that are unusual or unexpected.
Logistic regression is a type of statistical model that is used for classification tasks. The logistic regression algorithm models the relationship between a dependent variable (the label) and one or more independent variables (the features).
The algorithm then uses this model to make predictions on new data points. The predictions made by the logistic regression algorithm are probabilities that a data point belongs to a particular class.
"Logistic" refers to the Logit function, which is used to map probabilities between 0 and 1.
Naive Bayes is a simple but effective machine learning algorithm for classification tasks. The algorithm makes predictions by using the Bayes theorem, which is a statistical formula for computing conditional probabilities.
Naive Bayes is called "naive" because it makes the simplifying assumption that all features are independent of each other. This assumption, however, is not always true.
Linear discriminant analysis (LDA) is a type of supervised learning algorithm for classification tasks. LDA is also used for dimensionality reduction, which is the process of reducing the number of features in a dataset.
While LDA has a linear decision boundary, QDA is less strict and allows for a quadratic decision boundary. With more parameters, QDA allows for more complex modeling than LDA.
We’ve explored 15 algorithms in this article, but it’s not a comprehensive list. Other common machine learning algorithms include bagging, which is a type of ensemble algorithm, and gradient boosting, a fast and accurate algorithm commonly built with the XGBoost library. You may have also heard of techniques like principal component analysis (PCA), which is not a type of learner, but a way to reduce the dimensionality of large data sets.
As anyone who has tried to implement a machine learning model knows, there is a lot more to it than just the algorithm itself. You need to worry about things like data preprocessing for missing values, feature engineering, model selection and tuning, validation, deployment, and monitoring.
Not to mention the many decisions along the way about what software libraries and programming languages to use. It quickly becomes overwhelming, and it's easy to make a mistake that can ruin your whole project.
This is why end-to-end machine learning is so difficult. You need to have a deep understanding of the entire process in order to be successful. Instead of trying to hack it together in Python, or hiring a team of data science experts, you can use a platform like Akkio that takes care of the underlying work.
Akkio automates most of the machine learning process, so you can simply click to connect your data sources, choose your KPI, and let the platform do the rest. In a comparison between Google Cloud Platform, Microsoft Azure, Amazon Web Services, and Akkio, the latter was found to offer the most affordable, fastest, and easiest-to-use solution.
Beginners can follow our simple tutorials across forecasting, classification problems, regression problems, and more. Simply connect a training data set, and Akkio will select and optimize the right learning method for the problem at hand.
Let's walk through a simple example of classifying the sentiment of social media posts as positive or negative. We'll use a training data set of 1,000 comments, each labeled as positive or negative. Then, we'll use Akkio to automatically train and deploy a machine learning model that can classify tweets as positive or negative in real-time.
First, we'll need to connect our training data set in CSV format. As you can see below, the data set consists of two columns: "clean_text" and "category". The "clean_text" column contains the text of the review, and the "category" column contains the sentiment label.
Next, we'll need to select our target column, or the KPI we want to predict. Below, we can see that Akkio built a predictive model with around 90% accuracy.
Now that we've trained and deployed our machine learning model, we can start using it to automatically classify social media posts in real-time. To do this, we can deploy the model through Zapier, a no-code automation tool.
Zapier has integrations with thousands of apps, so you can deploy your machine learning models anywhere. For example, you could automatically classify incoming support tickets by connecting your Akkio model to Zendesk, or automatically send marketing leads to your CRM by connecting your model to Hubspot.
In a recent case study, a leading campaigning platform used Akkio to automatically and accurately target the right donors, resulting in 6 months of development time saved and 2.2X return compared to previous methods, supporting 5x annual revenue growth.
Other testimonials from customers show that Akkio is easy to use and provides rockstar results. Xavier Riley, SVP of Digital Strategy and Innovation at Standard Industries, said: "This democratization of data science is the easy button that many are looking for. It is easy to use and produces results that can make you look like a rockstar."
Akkio uses many of the algorithms mentioned above (with neural architecture search) to help business users leverage the power of AI without the burdens of long and expensive traditional AI solutions. Ajay Agarwal, partner at Bain Capital Ventures, said: "The best companies are leveraging AI across their enterprise. With Akkio, business users can leverage the power of AI without the burdens of long and expensive traditional AI solutions."
Akkio is particularly popular among marketing professionals. Aaron Doherty, Sr. Strategist of Growth Marketing & Analytics at MarketOne, said: "Akkio makes leveraging machine learning for marketing incredibly easy and they go above and beyond to help you if you get stuck." And at Ellipsis Marketing, Alex Denning said: "Akkio has let us build and deploy predictive models with no code, in no time. We've used Akkio to improve the performance of our internal processes: we can automate and improve decision making, saving time and improving results."