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A Guide to 4 Important Types of Machine learning With Use Cases
Artificial Intelligence (AI) has seen explosive growth in recent years, and the development of different types of Machine Learning (ML) has been a driving force behind it. The numbers speak for themselves: According to McKinsey, private equity and venture-capital funding in AI companies increased nearly fivefold from $16 billion in 2015 to $79 billion in 2022. It’s clear that businesses are eager to adopt AI/ML and explore its potential. However, with so many different types of machine learning available, it can be challenging to understand which one is best suited for a particular application. In this article, let’s take a closer look at the four main types of machine learning and their respective applications: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
1. Supervised Learning
Supervised learning involves using labeled datasets to train algorithms for accurate classification or outcome prediction. During training, machines use labeled data to predict output in the future. The labeled data helps set a strategic path for machines as they map inputs to the output. Additionally, analysts use test datasets to check the accuracy of the analysis after training continuously. Various industries such as healthcare, finance, and marketing widely use supervised learning
Types of Supervised Learning
Supervised machine learning can be classified into two types of problems: classification and regression. Classification algorithms are utilized when the output variable is binary or categorical, making them ideal for identifying whether emails are spam or legitimate. On the other hand, regression algorithms are utilized to make predictions like weather and market conditions for problems that involve a linear relationship between the input and output variables.
Applications of Supervised Learning
- Medical diagnosis
- Fraud detection
- Spam detection
- Speech recognition
2. Unsupervised Learning
Unsupervised learning is one of the four main types of machine learning techniques. It utilizes unlabeled and unclassified datasets to make predictions without human intervention. This method is useful for categorizing or grouping unsorted data based on similarities and differences, as well as discovering hidden patterns and trends in the input data. Unlike supervised learning, unsupervised learning algorithms learn from the data without any fixed output variable, making them suitable for complex tasks. They identify hidden patterns or groupings in the data; this makes them ideal for clustering, anomaly detection, and exploratory data analysis.
Types of Unsupervised Learning
Unsupervised learning can be classified into two main techniques: clustering and association. Cluster analysis is an unsupervised learning technique that involves grouping data points based on their similarities, differences, or features without prior knowledge or labels. Businesses commonly use this technique in applications such as retail marketing, email marketing, and streaming services to identify similarities and patterns.On the other hand, association is an unsupervised learning technique where machines discover interesting relations and connections among variables in large input datasets. Businesses commonly use associations for applications such as web usage mining, plagiarism checking, developing recommendations, and continuous production.
Applications of Unsupervised Learning
- Anomaly detection to identify fraud transactions
- Forecasting and predictive modeling
- Identifying and targeting market segments
- Recommendation systems to suggest products or services
3. Semi-Supervised Learning
Semi-supervised learning is a highly efficient and cost-effective machine learning technique combining labeled and unlabeled data during training. It allows machines to learn from all the available data by utilizing both supervised and unsupervised learning advantages. This method first uses unsupervised learning algorithms to group similar data. Thereafter, this labels previously unlabeled data, making the dataset more robust for training. Additionally, semi-supervised learning enables machines to improve their accuracy and performance.
Types of Semi-Supervised Learning
Semi-supervised machine learning includes self-supervised learning and multiple-instance learning. The self-supervised learning technique frames the problem as a supervised learning task to generate labeled data from unlabeled data. This approach is particularly useful when obtaining labeled data is relatively inexpensive. Additionally, it has shown impressive results in various applications. Notably, Google and Facebook utilize self-supervised techniques in computer vision and natural language processing.On the other hand, multiple-instance learning provides weakly supervised learning where bags containing training instances are labeled instead of individual instances. This approach allows leveraging weakly labeled data often present in business problems due to the high cost of labeling. Furthermore, it is useful in scenarios where only partial information is available and has been applied in various fields, including medical diagnosis, image classification, and natural language processing.
Applications of Semi-Supervised Learning
- Medical image analysis
- Speech recognition and natural language processing
- Text classification and sentiment analysis
- Fraud detection in finance
4. Reinforcement Learning
Reinforcement learning is a machine learning technique where an agent learns to take optimal actions through environmental feedback. Unlike other types of machine learning, this technique does not rely on labeled data. Instead, it utilizes a trial-and-error approach with a feedback-based process that allows the agent to learn from its experiences. One of the main advantages of reinforcement learning is its ability to learn from experience and improve performance over time.
Types of Reinforcement Learning
Reinforcement learning includes positive reinforcement learning and negative reinforcement learning. Positive reinforcement learning is a type of learning where the agent is rewarded for taking actions that lead to positive outcomes. The reward can be a numerical value, such as a score or a probability, or a symbolic value, such as a label or a tag. Negative reinforcement learning, on the other hand, is a type of learning where the agent is punished for taking actions that lead to negative outcomes. Here, the agent learns to avoid actions that result in punishment and instead takes actions that lead to positive outcomes.
Applications of Reinforcement Learning
- Building intelligent robotics
- Developing personalized treatment plans for patients
- Autonomous vehicles
- Game playing
ALSO READ: What is Classification in Machine Learning and Why is it Important?
Frequently Asked Questions
How Does Reinforcement Learning Differ from Supervised and Unsupervised Learning?
Reinforcement learning involves an agent learning to take actions that maximize a reward signal provided by the environment. Therefore, this learning process enables the agent to develop a policy that maps states to actions to achieve its goal. In contrast, supervised learning requires labeled data to learn how to make predictions, while unsupervised learning attempts to find patterns or structures in unlabeled data without any feedback signal.
What Types of Algorithms Fall Under the Category of Supervised Learning?
Common supervised learning algorithms include Linear Regression, Logistic Regression, Random Forest, Decision Trees, Naive Bayes, and Neural Networks. These perform various tasks such as regression, classification, and prediction, using labeled data for training.
What is Deep Learning, and How Does it Fit into the Different Types of Machine Learning?
Machine learning and deep learning are forms of AI. Specifically, machine learning is an AI that can learn and adapt automatically with minimal human intervention. On the other hand, deep learning is a subcategory of machine learning that uses artificial neural networks to imitate the human brain’s learning process.
ALSO READ: How AI is Changing Cybersecurity: New Threats & Opportunities
To conclude, it is evident that these types of machine learning are crucial components of the ever-growing digital world. As technology advances, the demand for professionals specializing in machine learning will only increase. If you’re interested in pursuing a career in this field, explore the machine learning and artificial intelligence courses Emeritus offers in collaboration with some of the world’s best universities.
By Krati Joshi
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