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As a notable example, some well known, high-quality Coursera content such as Andrew Ng’s classic Stanford Machine Learning course, Stanford’s Algorithms courses, and Andrew Ng’s Deep Learning specialization from deeplearning.ai are not included .

As a notable example, some well known, high-quality Coursera content such as Andrew Ng’s classic Stanford Machine Learning course, Stanford’s Algorithms courses, and Andrew Ng’s Deep Learning specialization from deeplearning.ai are not included .

The example of reinforcement learning is your cat is an agent that is exposed to the environment . The biggest characteristic of this method is that there is no supervisor, only a real number or reward signal. Two types of reinforcement learning are 1) Positive 2) Negative.

Reinforcement learning is a machine learning training method based on rewarding desired behaviors and/or punishing undesired ones . In general, a reinforcement learning agent is able to perceive and interpret its environment, take actions and learn through trial and error.

Reinforcement learning is a machine learning training method based on rewarding desired behaviors and/or punishing undesired ones . In general, a reinforcement learning agent is able to perceive and interpret its environment, take actions and learn through trial and error.

Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world .

The example of reinforcement learning is your cat is an agent that is exposed to the environment . The biggest characteristic of this method is that there is no supervisor, only a real number or reward signal. Two types of reinforcement learning are 1) Positive 2) Negative.