Machine Learning

What Is Machine Learning?

Machine learning is a well-known technique of data analysis that automates analytical model building. It’s a branch of artificial intelligence based on the concept that systems will learn from data, identify patterns and create decisions with minimal human intervention.

To simplify, we can say it is an application of Artificial Intelligence that is using data and need to make predictions. Above all, its main focus is on the development of computer programs that can access data and use it to learn for themselves.

Learn more about Machine Learning.

3 Types of Machine Learning algorithms

Supervised Learning

Supervised learning is the machine learning type that you will most likely encounter. It is the easiest to understand, where you can consider the learning is guided by a teacher. So we have a dataset that acts as a teacher and its role is to train the model or the machine. After the model is trained, it can certainly start making a prediction or decision when new data is given to it.

 Also, this type of learning is usually described as task-oriented because it is highly focused on a singular task.

Supervised Learning Examples: Regression, Decision Tree, Random Forest, Logistic Regression, etc.

Unsupervised Learning

Unsupervised learning is the opposite of supervised learning. Although we have no idea what our results should look like, it allows us to approach problems. Also, it is used for clustering population in different groups, which is commonly used for customer segmentation in different groups.

Unsupervised Learning Examples: Apriori algorithm, K-means, etc.

Reinforcement Learning

Important to realize, reinforcement learning is totally different from supervised and unsupervised learning. As a matter of fact, it is very behavior driven and it has influences from the fields of neuroscience and psychology.

Analytics Vidhy described this learning perfectly: „Using this algorithm, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions.“

Reinforcement Learning Example: Markov Decision Process

Picture 1. Machine learning algorithms

List of Common Machine Learning Algorithms

According to Analytics Vidha, below is a list of the most commonly used machine learning algorithms. Notably, they can be applied to almost any dana problem.

1. Linear Regression
2. Logistic Regression
3. Decision Tree
4. Naive Bayes
5. KNN
6. SVM
7. K-Means
8. Random Forest
9. Dimensionality Reduction Algorithms
10. Gradient Boost & AdaBoost