Below are some of the key concepts in Machine Learning.
Data: It is the foundation of ML. The models are trained using this, and the performance of the machine learning system is impacted directly by its quality and quantity.
Algorithms: These are the mathematical procedures or models that enable a machine to learn from the data. They constitute the central part of the learning process within machine learning.
Training: Feeding data to a machine-learning algorithm so it can "learn" patterns in the data. The model is trained to reduce errors or to increase its accuracy over time.
Model: The outcome of the training process is a machine learning model. It is the learned patterns and relationships in data, which can be used in making predictions or decisions based on new, unseen data.
Prediction: After training a model, one can use the trained model to make predictions about outcomes or decisions from never-before-seen new data.
Data: It is the foundation of ML. The models are trained using this, and the performance of the machine learning system is impacted directly by its quality and quantity.
Algorithms: These are the mathematical procedures or models that enable a machine to learn from the data. They constitute the central part of the learning process within machine learning.
Training: Feeding data to a machine-learning algorithm so it can "learn" patterns in the data. The model is trained to reduce errors or to increase its accuracy over time.
Model: The outcome of the training process is a machine learning model. It is the learned patterns and relationships in data, which can be used in making predictions or decisions based on new, unseen data.
Prediction: After training a model, one can use the trained model to make predictions about outcomes or decisions from never-before-seen new data.