Question

What is machine learning??​

Answer 1

Ответ:

it is a subset of AI which uses statistical methods to enable machines to improve with experience.

it is one of the most popular techniques to build AI systems to across the globe.

it is the science of getting machines to in the interpret, process and analyse in order to solve problems

Answer 2

I think it when robot, learn some thing as fotball or other

как то так но лудше дождитесь правильного ответа

Answer 3

Machine learning is a subset of artificial intelligence (AI) that involves the use of algorithms and statistical models to enable computer systems to improve their performance on a specific task through experience or data. In other words, machine learning allows computers to learn from data and make predictions or decisions based on that data, without being explicitly programmed to perform the task.

There are several types of machine learning algorithms, but they can be broadly categorized into three main types:

1. Supervised Learning: In supervised learning, the algorithm is trained on labeled data, meaning the input data is paired with the corresponding correct output. The algorithm learns to map the input data to the correct output during the training process. Once the model is trained, it can make predictions on new, unseen data.

2. Unsupervised Learning: Unsupervised learning involves training algorithms on unlabeled data. The system tries to learn the patterns and the structure from the data without any supervision. Clustering and association are common tasks in unsupervised learning.

3. Reinforcement Learning: Reinforcement learning is a type of machine learning where an agent learns how to behave in an environment by performing certain actions and receiving rewards or penalties. The agent learns to achieve a goal in an uncertain, potentially complex environment.

Machine learning algorithms are used in various applications, such as image and speech recognition, natural language processing, recommendation systems, autonomous vehicles, and many others. The effectiveness of machine learning models often depends on the quality and quantity of the data used for training, as well as the choice of the algorithm and its parameters.