Machine learning: Difference between revisions

#Supervised Learning: In supervised learning, an algorithm is trained on a labeled dataset where each input and its associated label are known. The goal is to learn a mapping between input and output so that the model can accurately predict future data without being informed beforehand.

#Unsupervised Learning: Unsupervised learning involves training an algorithm on an unlabeled dataset, where the output or label for each input is unknown. The goal is to uncover patterns or structure within the data by grouping similar examples together.

#Reinforcement Learning: Reinforcement learning is an approach in which an algorithm learns to make decisions by taking actions in an environment to maximize a reward signal. It receives feedback in the form of rewards or penalties, which it uses to adjust its decision-making strategy accordingly.

Machine learning involves many algorithms and models, such as linear regression: a straightforward method for predicting continuous output based on input features.

2. Logistic regression: an approach used to predict binary outcomes from given input features.

3. Decision trees: A tree-like model for making decisions or predictions based on input features.

4. Random forests: An ensemble of decision trees used for both regression and classification tasks.

5. Neural networks: Models inspired by the structure and function of the brain, used for various tasks such as image and speech recognition.

6. Support vector machines (SVMs): Classification method that finds boundary that best separates different classes in data set.

7. K-nearest neighbors (KNN): A classification algorithm that predicts the label of a new data point by looking at its closest neighbors in training data.

1. Image recognition and computer vision

2. Natural language processing

3. Speech recognition and synthesis

5. Fraud detection

6. Predictive maintenance

7. Healthcare 8. Financial services.

 

Machine learning is the process by which computers learn from data without being explicitly instructed what to do. It's like having a robot that can look at pictures of dogs and cats and figure out on its own how to tell them apart without ever seeing previous images. And this kind of intelligent robot could be employed for many different tasks such as comprehending conversations better, suggesting movies you might like, or even helping doctors make better decisions.

Imagine you have a computer program trying to figure out which toys you like best. At first, it doesn't know anything and therefore makes guesses based on rules given. However, as more toys are played with and feedback given about what works and doesn't works, the program gets better at anticipating future preferences - this process is known as "machine learning".