Machine Learning Overview

Basic Categorization of Machine Learning

In order to understand the whole picture of Machine Learning, we can use the following categorization. It is not perfect, but it shows a simple overview view of Machine Learning methods and algorithms.

Recently we are discussing this machine learning more than ever and a lot of people don’t have the exact idea or understanding of the different words and topics they see on the internet. Also, they don’t have an idea where to start learning machine learning due to the vast variety of these unfamiliar topics or words. So, basically, anyone can get the exact idea about what they have trouble with and start digging in.

ML Algorithm Categorization

Machine learning techniques and algorithms can be classified into four main types based on the learning approach they employ. These categories are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Let’s explore each category in detail:

1. Supervised Learning:

• Regression: This involves predicting continuous values, such as predicting house prices based on features like square footage, number of bedrooms, etc.
• Classification: Here, the goal is to assign predefined labels to input data, such as classifying emails as spam or non-spam.

2. Unsupervised Learning:

• Clustering: This technique aims to identify inherent patterns or groups in the data, grouping similar items together without predefined labels.
• Dimensionality Reduction: It involves reducing the number of input features while preserving important information to simplify the data and improve computational efficiency.
• Association Rule Learning: This technique discovers interesting relationships or patterns among variables, often used in market basket analysis or recommendation systems.

3. Semi-Supervised Learning:

• This approach leverages both labelled and unlabelled data for training. It combines elements of supervised and unsupervised learning to make predictions on partially labelled datasets.
• Examples include self-training, where the model learns from labelled data and then uses that knowledge to label unlabelled data, and co-training, where two or more models learn from different feature sets or views of the data.

4. Reinforcement Learning:

• This learning paradigm involves an agent interacting with an environment and learning from feedback in the form of rewards or punishments.
• The agent takes actions in the environment and receives feedback, allowing it to learn the optimal sequence of actions to maximize rewards.
• Algorithms like Q-Learning, Deep Q-Networks (DQN), and Policy Gradient Methods are commonly used in reinforcement learning.

It’s worth noting that some algorithms can be adaptable to multiple learning approaches. For instance, decision trees can be utilized for both supervised (classification) and unsupervised (clustering) learning tasks. Additionally, hybrid models that combine different learning approaches, such as generative adversarial networks (GANs), continue to emerge in the field to address specific challenges and domains.

These categories provide a general overview of machine learning techniques and algorithms, but it’s important to remember that the field is dynamic, with ongoing research and development to tackle new problems and advance the capabilities of machine learning systems. You can have look at here to get basic understanding about how things work.