As machine learning advances, its models offer companies new solutions that can help them become competitive. However, with so many models available, you might be unsure as to which algorithm you should select.
If you desire to use innovation to advance your business, you'll first need to understand how machine learning algorithms work. Once you've gained an understanding of their techniques, you'll know how they can be useful to your business.
In this blog, we'll explore these algorithms and reveal the top 10 so you can leverage them to gain an advantage in your industry.
Machine Learning is a subdivision of artificial intelligence. Software developers use machine learning algorithms to create models that can be trained to find patterns and predict results. These models can be trained on labelled and unlabeled data sets and can improve their performance over time.
You can hire software companies to create algorithms for businesses to help you find solutions to your problems. For instance, a financial institution can use an algorithm to detect fraud.
Before you can examine which is the best program that uses machine learning, you need to understand how it works. These algorithms can perform different tasks based on how they're trained and the techniques they use to manipulate data.
You can classify these algorithms into these 4 categories:
Supervised learning algorithms are programs that require you to train them so they can learn what output they should give you. You'll act as a teacher, giving it labelled data and checking that the output is what you desire.
This type of algorithm uses these techniques:
Classification: This technique involves categorizing data into a predetermined set of groups. For instance, this algorithm is useful for diagnosing by categorizing symptoms into diseases.
Regression: With this technique, an algorithm 'studies' and predicts continuous values. If you have an aviation company, you can use this technique to predict the weather forecast and tailor your flight schedule accordingly.
Decision Tree: Algorithms that use this technique operate based on conditions or rules and take into account past outcomes. For instance, financial institutions can use this technique to approve loans based on their rules.
Another type of program is the unsupervised learning algorithm. As the name suggests, this algorithm doesn't need you to supervise it. Instead, it learns from unlabeled and uncategorized data, which is easier and cheaper to gather than its counterpart. It works by finding insights, commonalities, or patterns in the information you give it.
You can use it with these techniques:
Clustering: The clustering technique groups similar data based on characteristics that the algorithm notices. This program can process images.
Random Forest: This technique works similarly to the supervised decision tree. It contains a collection of decision trees that combine data sets into categories. To better understand this, consider how multiple data points in an individual's life can pinpoint their credit score.
Semi-supervised algorithms work partially like supervised and unsupervised ones. You can train them on data that's mostly unlabeled. Then, it will use the insights it has received from the labelled data to understand that the rest of the data also has characteristics it can use to categorize it. Since labelling data is time-intensive and requires expertise, this type of algorithm helps you minimize some of the costs of gathering labelled data.
With this type of algorithm, you can use these machine-learning techniques:
Pseudo-Labeling: With this technique, you train the algorithm on data that has labels. Once it has gained insights about the labelled data, you can use the program to categorize the rest of the data based on previously gained insights. You could use this technique to diagnose based on information on patients' symptoms.
Cluster-then-label: This involves grouping labelled and unlabeled data into clusters based on their characteristics. Then, the algorithm can use the labelled data to attach labels to the rest. For example, if you received a large data set about a group of individuals as an advertising company, labelling it all would be resource-intensive. However, you need that information labelled so you can target. You can use this algorithm to find commonalities between the individuals, and you'll be able to target them by group.
Like unsupervised algorithms, reinforcement machine learning models don't need to be supervised. Instead, you train them through rewards. You can give them a positive reward when they arrive at the right conclusion. If not, you give them a negative reward. This encourages the algorithm to make the right decisions.
With reinforcement algorithms, you can use these techniques:
Q-Learning: This technique trains algorithms to find the optimal next action based on the current conditions. For instance, you can use this technique to change how your online advertising appears to potential customers to prioritize advertisements that generate clicks.
Markov Decision Process: With this technique, the algorithm learns to take actions based on the rewards it previously received. Then, you can train with rewards to prefer certain actions to maximize its reward. For example, you could train a program using this technique to know the threshold of cancer cells in a patient before recommending no action, treatment, or surgery.
Now that you have an understanding of these algorithms and how they can help your business, here are the 2024 top picks you can purchase:
Here's a breakdown of these algorithms' types and techniques.
Algorithm | Algorithm Type | Technique |
|---|---|---|
Linear Regression | Supervised | Regression |
Logistic Regression | Supervised | Regression |
Decision Trees | Supervised | Decision Trees |
SVM | Supervised | Classification |
Naive Bayes | Supervised | Classification |
KNN | Supervised | Classification |
K-Means Clustering | Unsupervised / Reinforcement | Clustering |
Q-Learning | Unsupervised / Reinforcement | Q-Learning |
Apriori | Unsupervised | Association |
Random Forest | Supervised / Unsupervised | Random Forest |
Now that you know the best ML algorithms of 2024, from Linear Regression to Random Forest, you can take your business to new heights with the discussed algorithms and the techniques they use. In particular, they can help you find patterns to guide your decision-making. With these technological advancements, you have a variety of options depending on the kind of task you need to complete and whether you label your data. Plus, each type of algorithm has multiple techniques you can use such as predicting future prices or classifying information with a supervised algorithm.
