Artificial Intelligence Reinforcement Learning in Python Free Download. This course requires a solid foundation in calculus (derivatives), probability, and proficiency in Python with Numpy and Matplotlib. Beneficially, experience with a few supervised machine learning methods, gradient descent, and good object-oriented programming skills are recommended. The course delves into the foundations of AI technologies like OpenAI ChatGPT and GPT-4, moving beyond the realm of supervised and unsupervised machine learning to explore the exciting possibilities of reinforcement learning.
Reinforcement learning, gaining popularity for its applications in complex tasks like playing chess, driving cars, and superhuman video game play, reveals insights from behavioral psychology and neuroscience. The course covers essential topics such as the multi-armed bandit problem, Markov Decision Processes (MDPs), dynamic programming, Monte Carlo, and Temporal Difference (TD) Learning, including Q-Learning and SARSA. It also explores approximation methods, demonstrating how to integrate deep neural networks into RL algorithms. Practical application is emphasized through projects, including building a stock trading bot using Q-Learning.
The course stands out for its focus on implementation, echoing the philosophy that understanding is demonstrated through hands-on creation. The instructor distinguishes the course from others, emphasizing the importance of not just plugging data into libraries but learning to implement machine learning algorithms from scratch. Artificial Intelligence Reinforcement Learning in Python Free Download. Suggested prerequisites include calculus, probability, object-oriented programming, Python coding proficiency, Numpy operations, linear regression, and gradient descent. The course caters to anyone eager to explore AI, data science, machine learning, and deep learning, targeting both students and professionals seeking a comprehensive understanding of advanced AI techniques.
Artificial Intelligence Reinforcement Learning in Python Download
Reinforcement Learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent takes actions, and based on those actions, the environment provides feedback in the form of rewards or penalties. The goal of the agent is to learn the optimal sequence of actions that leads to the maximum cumulative reward over time.
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