What is Machine Learning?
Machine learning is a branch of artificial intelligence that focuses on developing systems that can automatically learn from data. Rather than being explicitly programmed to perform a specific task, machine learning systems use algorithms that allow them to find patterns and trends in the data they receive, and use that knowledge to improve their performance on a given task.
- A decision process: A recipe of calculations or other steps that takes in the data and “guesses” what kind of pattern your algorithm is looking to find.
- An error function: A method of measuring how good the guess was by comparing it to known examples (when they are available). Did the decision process get it right? If not, how do you quantify “how bad” the miss was?
- An updating or optimization process: A method in which the algorithm looks at the miss and then updates how the decision process comes to the final decision, so next time the miss won’t be as great.
There are different types of machine learning, depending on the type of learning that the systems use.
- Supervised Learning is a type of machine learning where the model is trained on labeled data to make predictions or take actions based on input data. The goal is to learn a mapping function from input variables to the output variables, so that for new unseen data, the model can predict the output accurately. In supervised learning, the model is provided with labeled examples of the inputs and the expected outputs during training and the model is updated based on the errors made in predictions during the training process. Some common applications of supervised learning include image classification, sentiment analysis, and linear regression.
- Unsupervised Learning is a type of machine learning where the model is trained on an unlabeled dataset and the goal is to uncover hidden patterns or relationships within the data. Unlike supervised learning, there are no pre-existing labels or outputs for the model to learn from. Instead, the model must learn to identify structures and relationships within the data without the guidance of pre-existing labels. Some common applications of unsupervised learning include clustering, dimensionality reduction, and anomaly detection. In clustering, the goal is to group similar data points together. In dimensionality reduction, the goal is to reduce the number of features in the data while retaining as much information as possible. In anomaly detection, the goal is to identify data points that do not conform to the general patterns in the data.
- Reinforcement Learning is a type of machine learning where an agent interacts with an environment and learns to make decisions based on rewards and penalties it receives. In this type of learning, the agent takes actions in an environment to maximize a reward signal. The agent learns from the consequences of its actions and updates its policy accordingly. Reinforcement learning can be used to solve a wide range of problems, including game playing, robotics, and decision making under uncertainty. Reinforcement learning is different from supervised and unsupervised learning in that the training data consists of sequences of interactions between the agent and the environment, rather than a labeled dataset. In reinforcement learning, the goal is to learn a policy, which is a mapping from states of the environment to actions taken by the agent, that maximizes the expected cumulative reward over time.