As a request from my friend Richaldo, in this post I’m going to explain the types of machine learning algorithms and when you should use each of them. I particularly think that getting to know the types of Machine learning algorithms is like getting to see the Big Picture of AI and what is the goal of all the things that are being done in the field and put you in a better position to break down a real problem and design a machine learning system. Show
Terms frequently used in this post:
Types of machine learning AlgorithmsThere some variations of how to define the types of Machine Learning Algorithms but commonly they can be divided into categories according to their purpose and the main categories are the following:
Supervised Learning
Draft
List of Common Algorithms
Unsupervised Learning
Draft
List of Common Algorithms
Semi-supervised LearningIn the previous two types, either there are no labels for all the observation in the dataset or labels are present for all the observations. Semi-supervised learning falls in between these two. In many practical situations, the cost to label is quite high, since it requires skilled human experts to do that. So, in the absence of labels in the majority of the observations but present in few, semi-supervised algorithms are the best candidates for the model building. These methods exploit the idea that even though the group memberships of the unlabeled data are unknown, this data carries important information about the group parameters. Reinforcement Learningmethod aims at using observations gathered from the interaction with the environment to take actions that would maximize the reward or minimize the risk. Reinforcement learning algorithm (called the agent) continuously learns from the environment in an iterative fashion. In the process, the agent learns from its experiences of the environment until it explores the full range of possible states. Reinforcement Learning is a type of Machine Learning, and thereby also a branch of Artificial Intelligence. It allows machines and software agents to automatically determine the ideal behavior within a specific context, in order to maximize its performance. Simple reward feedback is required for the agent to learn its behavior; this is known as the reinforcement signal. There are many different algorithms that tackle this issue. As a matter of fact, Reinforcement Learning is defined by a specific type of problem, and all its solutions are classed as Reinforcement Learning algorithms. In the problem, an agent is supposed decide the best action to select based on his current state. When this step is repeated, the problem is known as a Markov Decision Process. In order to produce intelligent programs (also called agents), reinforcement learning goes through the following steps:
List of Common Algorithms
Use cases:Some applications of the reinforcement learning algorithms are computer played board games (Chess, Go), robotic hands, and self-driving cars. Final NotesThere is possible to use different criteria to classify types of machine learning algorithms but I think using the learning task is great to visualize the big picture of ML and I believe according to your problem and the data you have in hand you can easily decide if you will use Supervised, unsupervised or reinforcement learning. In the upcoming posts I’ll give more examples about each type of machine learning algorithms. This image from en.proft.me below might help you. Further Readings
Let me know what you think about this, if you have suggestion of a topic you would love to see here get in touch. Last ThingIf you enjoyed the writings leave your claps 👏 to recommend this article so that others can see it. What is a label in machine learning?Labels. A label is the thing we're predicting—the y variable in simple linear regression. The label could be the future price of wheat, the kind of animal shown in a picture, the meaning of an audio clip, or just about anything.
What identifies patterns in data including outliers uncovering the underlying structure?Data mining, also known as knowledge discovery in data (KDD), is the process of uncovering patterns and other valuable information from large data sets.
How does machine learning find patterns in data?Pattern recognition is the use of machine learning algorithms to identify patterns. It classifies data based on statistical information or knowledge gained from patterns and their representation. In this technique, labeled training data is used to train pattern recognition systems.
What is labeled and unlabeled data in machine learning?Labeled data is used in supervised learning, whereas unlabeled data is used in unsupervised learning . Labeled data is more difficult to acquire and store (i.e. time consuming and expensive), whereas unlabeled data is easier to acquire and store.
|