Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on developing algorithms and statistical models to enable computers to perform tasks without being explicitly programmed for those tasks. The primary goal of machine learning is to allow computers to learn from data and make predictions or decisions based on that learning.
There are several types of machine learning approaches:
Supervised Learning: In supervised learning, the algorithm is trained on labeled data, where each example in the training dataset is paired with the correct output. The algorithm learns to map the input to the output, making predictions or decisions based on that mapping. Common supervised learning tasks include classification and regression. (Email spam detection, medical diagnosis)
Unsupervised Learning: Unsupervised learning involves training algorithms on unlabeled data. The algorithm learns patterns and structures in the data without explicit guidance on what to look for. Common unsupervised learning tasks include clustering, dimensionality reduction, and association rule learning. The demonstration for this workshop features 2 examples of unsupervised learning. (Customer segmentation)
Semi-Supervised Learning: Semi-supervised learning combines elements of supervised and unsupervised learning. In this approach, the algorithm is trained on a dataset that contains both labeled and unlabeled data. The algorithm learns from the labeled data while also leveraging the additional information present in the unlabeled data to improve its performance. (Image labeling)
Reinforcement Learning: Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on its actions, and its goal is to learn the optimal behavior that maximizes cumulative rewards over time. Reinforcement learning is often used in scenarios such as game playing, robotics, and autonomous driving.
Deep Learning: Deep learning is a subset of machine learning that focuses on artificial neural networks with multiple layers (deep neural networks). Deep learning algorithms automatically learn hierarchical representations of data by progressively extracting higher-level features from raw input. Deep learning has achieved remarkable success in various tasks such as image recognition, natural language processing, and speech recognition. ChatGPT is built on multiple deep learning networks.
These are the primary types of machine learning approaches, each suited to different types of tasks and data. Machine learning techniques are widely used across various industries and domains, driving advancements in technology and enabling automation and decision-making capabilities in diverse applications.
