Types of Machine Learning
Machine learning algorithms form the backbone to artificial intelligence, offering diverse techniques to solve wide range of real world problems. They are broadly categorized into supervised, unsupervised, semi-supervised and reinforcement learning, each type of algorithm has its unique strengths and use cases.
In addition to these categories, specialized algorithms like ensemble methods (e.g., bagging and boosting) and deep learning models (e.g., convolutional neural networks for image recognition) offer cutting-edge solutions for highly complex tasks.
Selecting the right algorithm depends on -
- the nature of the input data,
- the problem's complexity, and
- the desired outcome.
Mastering these algorithms will empower data scientists and developers to push the boundaries of innovation in AI-powered applications.
The following is explained briefly about types of machine learning and each of its types are explained further in upcoming section.
1. Supervised Learning
Supervised learning involves training a model on labeled data. This means the dataset contains input-output pairs where the output (target variable) is already known. It predicts the data by mapping inputs to their corresponding output.
How supervised ML works ? A labeled dataset is provided, such as email data tagged as "spam" or "not spam." The model learns patterns in the data during training. After training, it predicts the output for new, unseen inputs.
There are two types of supervised learning such as classification and regression and each of them were explained in this blog.
2. Unsupervised Learning
Unsupervised learning deals with unlabeled data (meaning dataset contains only input or feature values). It finds hidden patterns, grouping and structures within the data without predefined categories.
How unsupervised ML works ? The data has no predefined labels or outputs. The model groups or clusters data points based on similarities.
Imagine you own an online store and have a dataset with customer age, income, and spending habits. Using clustering, you can group customers into categories like "budget shoppers," "moderate spenders," and "high-value customers."
These are broadly divided into three types such as clustering, association rule mining and dimensionality reduction and each of them were explained in this blog.
3. Semi-supervised Learning
Semi-supervised learning is a hybrid approach that uses a small amount of labeled data along with a large amount of unlabeled data. It is particularly useful when labeling data is expensive or time-consuming.
How semi-supervised works ? The model uses the labeled data to learn initial patterns and then applies those insights to infer labels or structure in the unlabeled data, improving its overall performance.
The best example is a photo-sharing app that uses a few labeled images to teach a model to categorize millions of unlabeled pictures.
Self-training, co-training, and graph based training are some of the algorithms used for this type. For more details please visit this blog.
4. Reinforcement Learning
Reinforcement learning (RL) is a goal-oriented learning technique. Here, an agent interacts with an environment, learns from feedback in the form of rewards or penalties, and optimizes its actions to achieve maximum reward.
How reinforcement learning works ? An agent takes actions in an environment. Based on the action, the agent receives a reward (positive feedback) or penalty (negative feedback). Over time, the agent learns the optimal strategy to maximize rewards.
For example, think of a robotic vacuum cleaner. Initially, it may bump into walls, but over time, it learns the layout of your home and cleans efficiently without hitting obstacles.
Q-Learning, Deep Q-Networks (DQN), and Policy Gradient Methods are some of the algorithms popularly used for this type of learning. To know further details of reinforcement learning visit this blog.