Machine learning is the foundation that companies are using to gain insights on their customers, products, costs, and revenues. A well-known example of machine learning is Netflix’s algorithms which are designed to give movie suggestions based on your past viewing history.
To provide you with a brief introduction to these algorithms, here are the most common types of machine learning algorithms and their real-world applications.
There are several different ways that an algorithm can model a problem as a result of its interaction with the learning environment or the input data. Here are the four different learning styles in machine learning algorithms:
Now that you have a general understanding about the types of machine learning tasks, here are some of the most popular algorithms that are used to solve business problems along with their real-life applications.
A decision tree is a supervised machine learning approach that uses a tree-like graph or model of decisions and their potential outcomes, which may include chance-event outcomes, utility, and resource costs. This method makes it possible to approach a problem in a structured way in order to arrive at a logical conclusion.
Decision trees can also be used to assess the number of questions that need to be asked in order to determine the probability of making the right decision in most instances.
Decision trees can be used in real-world applications such as:
The most popular decision tree algorithms are:
Logistic regression is a supervised machine learning technique that measures the relationship between a categorical dependent variable and independent variables. It estimates probabilities by using a logistic function, which is the cumulative logistic distribution.
Logistic regression is used in real-world applications such as:
These are supervised machine learning classification algorithms that are based on Bayes’ theorem which is useful for large data sets. It is also a good option for instances where there are limited CPU and memory resources.
Bayesian classification is used in real-world applications such as:
The most popular Bayesian algorithms are:
SVM is a supervised machine learning technique that is used for pattern recognition and classification problems. The data must have two classes.
SVM is used in real-world applications such as:
Clustering is an unsupervised machine learning approach. It involves the task of creating clusters, or groups, with sets of objects that are more similar to each other than to those in the other groups.
The most popular clustering algorithms are:
Clustering is used in real-world applications such as:
According to the problem and the data you have available to you, you’ll need to make a decision as to which machine learning algorithms to use. A typical question asked by beginners to machine learning, is “which algorithm should I use?” The answer to this question will depend on many variables, including the data, time limitations, and what you ultimately want to do with the data.
Even an experienced data scientist won’t be able to determine which algorithm will perform best before trying different algorithms. Sometimes more than one algorithm will apply and in other instances none of these will be a perfect match.
Despite these challenges, our team will find a solution for your needs. If you are interested in learning more about how we can implement machine learning for your project, contact us today for a free consultation.
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