With the constant advancements in artificial intelligence, the field has become too big to specialize in all together. There are countless problems that we can solve with countless methods. Knowledge of an experienced AI researcher specialized in one field may mostly be useless for another field. Understanding the nature of different machine learning problems is very important. Even though the list of machine learning problems is very long and impossible to explain in a single post, we can group these problems into four different learning approaches:

  • Supervised Learning;
  • Unsupervised Learning;
  • Semi-supervised Learning; and
  • Reinforcement Learning.

Before we dive into each of these approaches, let’s start with what machine learning is:

What is Machine Learning?

The term “Machine Learning” was first coined in 1959 by Arthur Samuel, an IBM scientist and pioneer in computer gaming and artificial intelligence. Machine learning is considered a sub-discipline under the field of artificial intelligence. It aims to automatically improve the performance of the computer algorithms designed for particular tasks using experience. In a machine learning study, the experience is derived from the training data, which may be defined as the sample data collected on previously recorded observations or live feedbacks. Through this experience, machine learning algorithms can learn and build mathematical models to make predictions and decisions.

The learning process starts by feeding training data (e.g., examples, direct experience, basic instructions) into the model. By using these data, models can find valuable patterns in the data very quickly. These patterns are -then- used to make predictions and decisions on relevant events. The learning may continue even after deployment if the developer builds a suitable machine learning system which allows continuous training.

Four Machine Learning Approaches

Top machine learning approaches are categorized depending on the nature of their feedback mechanism for learning. Most of the machine learning problems may be addressed by adopting one of these approaches. Yet, we may still encounter complex machine learning solutions that do not fit into one of these approaches.

This categorization is essential because it will help you quickly uncover the nature of a problem you may encounter in the future, analyze your resources, and develop a suitable solution.

Let’s start with the supervised learning approach.

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4 Machine Learning Approaches that Every Data Scientist Should Know
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