Machine Learning Topic 4 : Types of Machine Learning
Hafiz Ahsan Ashfaq
Microbiologist | Molecular Biologist | Forensic Scientist | Bioinformatician | Data Scientist | Business Developer | PhD Position Seeker | Academic Writer | Content Writer |
Machine learning (ML) is a must-have component of artificial intelligence whereby it trains systems on data, makes decisions, and improves over time. In this article, we explore the four major types of machine learning that include Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, and Reinforcement Learning. We will then introduce key algorithms for each type and provide examples to help you understand these concepts.
1. Supervised Learning
It is a process of learning with a teacher. Here, the algorithm will be trained on labeled data. In other words, this simply means not only that input data are given but also the respective output-or labels. The aim in supervised learning is that the model learns to do a mapping from inputs to outputs so that it can predict future outputs, based on new inputs.
How it works:
Labeled Data: The input data is fed into the algorithm and also includes the right outputs for them.
The algorithm learns from this information and can make predictions on inputs that it has not seen before.
Example:
Suppose you have a list of fruits in which each fruit is labeled as "apple," "banana," or "orange." The algorithm will learn to tie features like color, size, and shape with one of the fruit labels.
Types of Supervised Learning:
Example: Spam or not spam classify an email.
Algorithms: Logistic Regression, KNN, SVM, Decision Trees, Random Forest.
Example: House price forecast based upon size and location
Algorithms: Linear Regression, Polynomial Regression, Ridge Regression, Lasso Regression.
Popular Algorithms in Supervised Learning:
2. Unsupervised Learning
In unsupervised learning, the input data is not labeled, and the model's goal is to identify hidden patterns or groupings in the data. This type of learning is used when we don't have labeled data, and the algorithm needs to learn the structure of the data itself.
How It Works:
No Available Labeled Data: The output is generated based on data so that it could find out some relationships or structure, but there is no consideration of labeled outputs to guide the algorithm.
The model groups similar points of data together with clusters around certain features.
Example:
Suppose you had this mixed dataset of fruits and fed this into the algorithm, but did not specify what fruit belongs to which class. In other words, you are not specifying what kind of fruit each one is. The algorithm would go ahead and group similar fruits, grouped by attributes like color and size, but would not know their actual fruit names.
Types of Unsupervised Learning:
Clustering: It is grouping data based on similarities.
For instance, in marketing: There is an application known as customer segmentation by which several customer groups with similar behavior can be identified
Algorithms: K-Means, Hierarchical Clustering, DBSCAN.
Dimensionality Reduction: It reduces the number of features without losing important information.
Example: Reducing the number of features in the image dataset to make computations faster.
Algorithms: PCA, Singular Value Decomposition (SVD).
Common Algorithms in Unsupervised Learning:
K-Means Clustering: Groups data into ?? clusters based on similarity.
Hierarchical Clustering: Develops a tree-like model of clusters through a hierarchy
Principal Component Analysis (PCA): Used for dimensionality reduction which reduces data
dimension to make it easier to analyze
3. Semi-Supervised Learning
Semi-supervised learning is the halfway point between supervised and unsupervised learning; here, we use a small amount of labeled data along with a large amount of unlabeled data. This approach comes handy when acquiring large quantities of labeled data is a costly affair or very time-consuming and yet a large pool of unlabeled data is available with us.
领英推荐
How it Works
The algorithm begins with a small set of labeled data and a much larger number of unlabeled data.
Learning patterns from the labeled data, it then uses those patterns to label the number of unlabeled data, thereby improving upon its learning.
Example:
In a medical images dataset, 30% of the images are labeled, for instance, as "tumor" or "no tumor," whereas 70% are unlabeled. This helps the model learn from the labeled and unlabeled data, thereby enhancing its performance in detecting a tumor.
Common Algorithms in Semi-Supervised Learning:
Self-training : The model iteratively labels its own data.
Co-training: Two or more models are trained on different views of the data and help each other label unlabeled data.
Generative Models: Models like Variational Autoencoders (VAEs) learn the underlying structure of the data to predict missing labels.
4. Reinforcement Learning
Reinforcement learning, or RL, is teaching a machine how to do the tasks by rewarding good behavior and punishing bad behavior. Under such learning, an agent interacts with an environment to discover by trial and error how to maximize rewards earned.
How it Works:
The model, (or 'agent') takes actions in an environment
Based on the outcome of its actions, it receives feedback in the shape of rewards or penalties.
The objective is to learn an optimal policy which maximizes cumulative rewards over the long term.
Example:
Imagine playing a game where the AI agent learns how to drive a character by receiving positive rewards for winning and negative rewards for losing. Using experiences over time, it learns the best strategies to ensure a win.
Basic Applications of Reinforcement Learning
Autonomous Vehicles: Mastering the navigation of roads safely
Robotics: Teaches robots to pick objects.
Gaming: AI agents learn to play games such as chess or Go.
Common Algorithms in Reinforcement Learning
Q-Learning: A value-based algorithm where the agent learns the value of every action in a given state.
Deep Q-Network (DQN): A neural network-based extension of Q-Learning.
Policy Gradient Methods: Direct optimisation of rewards by learning a policy.
Proximal Policy Optimization (PPO): A policy gradient method that is guaranteed to give stable learning.
Conclusion
To summarize, several approaches are covered under the umbrella term machine learning, each suited to specific types of tasks:
Supervised Learning: Learning when exemplar outputs are already provided for labelled data in order to make predictions.
Key Algorithms: Logistic Regression, Decision Trees, KNN.
Unsupervised Learning: Finding patterns in unlabeled data.
Key Algorithms: K-Means Clustering, PCA.
Semi-Supervised Learning: Labeled and unlabeled data to become used together in order to improve learning.
Key Algorithms: Self-training, Co-training.
Reinforcement Learning: Learning by trial and error through rewards and penalties.
Key Algorithms: Q-Learning, DQN, PPO.
Understanding these types of machine learning and their algorithms is crucial for applying AI solutions effectively across various domains - healthcare, finance, robotics, and more. The better you are familiar with these learning types, the simpler it will be to pick up the appropriate way for your specific problem.