Machine Learning Basics
Machine Learning is a subfield of artificial intelligence (AI) that focuses on the development of algorithms and models that allow computers to learn from and make predictions or decisions based on data. It is used in a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, and more.
In order to understand Machine Learning, it is important to have a good grasp of some key concepts and terminologies. Let's explore a few of them:
Supervised Learning: This is a type of Machine Learning where the algorithm learns from labeled examples that provide the correct answer or prediction.
Unsupervised Learning: In contrast to supervised learning, unsupervised learning algorithms learn from unlabeled data and identify patterns and relationships in the data.
Model: A machine learning model is a mathematical representation of a real-world process or problem. It is trained on a dataset to make predictions or decisions.
Feature: In the context of machine learning, a feature is an individual measurable property or characteristic of the data that is used as an input to a machine learning algorithm.
Training Data: The data used to train a machine learning model. It consists of examples or instances with the corresponding labels or target values.
Testing Data: Data that is separate from the training data and is used to evaluate the performance of a trained machine learning model.
Understanding these concepts will serve as a foundation for exploring different machine learning algorithms and techniques. In the upcoming lessons, we will dive deeper into supervised learning, unsupervised learning, model evaluation, and more.
