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Currently, the success of machine learning is the result of its potential to learn how to identify patterns in vast amounts of data and make accurate predictions using such data. This is a clear advantage in terms of business competitiveness since it’s never been easier for companies to harness data.
The crux of the matter is to clearly define the goal. It is not until the goal is set that we can decide what type of learning method will be used.
Different types of Machine Learning
We can define three different types of machine learning methods, depending on the problem that must be solved: supervised machine learning, unsupervised machine learning, and reinforcement learning. In this article, we will explain how they work, giving a few application examples.
Supervised Machine Learning
This is the most common type. In this case – where the output is clearly defined-, the algorithms work with labeled data that we use to supervise the training process. The algorithm learns from the data history to assign the correct output for a new value, in other words, it predicts the output.
Supervised machine learning can be used to classify problems, such as digit identification, diagnostics or fraud detection, and regression problems, such as weather or economic forecasts. The main difference between them is the target variable (categorical in classification cases and numeric in regression cases).
Below are some of the most commonly used algorithms:
- Decision Trees
- Naïve Bayes Classifier
- Linear Regression
- Logistic Regression
- Support Vector Machines (SVM)
- Ensemble Methods
Unsupervised Machine Learning
Unsupervised Machine Learning uses historical data that has not been labeled, so the computer is not expected to explicitly learn such data. It goes into the problem blind, trying to describe the inherent structure of the data to find patterns from the data analysis.
Clustering is the most common problem for which unsupervised learning can be used, along with dimensionality reduction. In the first case, it can be used in recommender systems or market segmentation, whereas in the second case, it is applied to Big Data visualization or meaningful comprehension.
The most common Unsupervised Machine Learning algorithms are:
- Cluster Analysis
- Principal Component Analysis
- Singular Value Decomposition
- Independent Component Analysis
Reinforcement Machine Learning
Reinforcement learning is described as a reward-based system, where an agent must learn how to behave so as to minimize risks and maximize its rewards in the future (performing actions and checking the results).
Common reinforcement learning algorithms include:
- Deep Adversarial Networks
- Temporal Difference.
This type of machine learning method is applicable to:
- Game AI
- Skill acquisition
- Learning tasks
- Robot navigation
- Real-time decision-making
We can define three different types of machine learning methods, depending on the problem that must be solved.
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