About This Course
Machine Learning. The use and development of computer systems that are able to learn and adapt without following explicit instructions. That by using algorithms and statistical models to analyse and draw inferences from patterns in data.
Machine learning (ML) is a branch of artificial intelligence that enables computers to learn from data. And make predictions or decisions without being explicitly programmed to do so. In other words, machine learning algorithms learn from past data to make predictions about future data.
There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
Supervised learning is a type of machine learning in which an algorithm learns to map input data (also called features) to output data (also called labels or targets). The algorithm is trained on a dataset that contains both input and output data, and the goal is to learn a function that can predict the output given new input data. Examples of supervised learning tasks include image classification, speech recognition, and fraud detection.
Unsupervised learning is a type of machine learning in which an algorithm learns to find patterns in the input data without any explicit supervision or guidance. The algorithm is trained on a dataset that contains only input data, and the goal is to discover structure or relationships in the data. Examples of unsupervised learning tasks include clustering, anomaly detection, and dimensionality reduction.
Correspondingly, reinforcement learning is a type of machine learning in which an algorithm learns to make decisions by interacting with an environment. The algorithm receives feedback in the form of rewards or penalties based on its actions. And the goal is to learn a policy that maximizes the cumulative reward over time. Examples of reinforcement learning tasks include game playing, robotics, and autonomous driving.
Machine learning algorithms can be further classified based on their model architecture. Including linear models, decision trees, neural networks, and more. Neural networks, in particular, have gained a lot of attention in recent years due to their ability to model complex non-linear relationships between inputs and outputs.
To train a machine learning algorithm, data is typically split into training and testing sets. The algorithm trains on the training set, and the performance is evaluated on the testing set. The goal is to develop a model that generalizes well to new, unseen data.
Machine learning has a wide range of applications in fields such as computer vision. Natural language processing, speech recognition, recommender systems, and more. It has the potential to automate tasks. That were previously done by humans, leading to increased efficiency and productivity in various industries.
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