We are living in a new age of widespread remote, online learning. Whether it's homeschool parents turning to online resources to help plan lessons, new families looking for activities for their housebound kids over the summer, or high schoolers looking for additional test prep help, the internet is becoming a virtual classroom for a growing number of kids. And the good news is, the quality of online learning platforms has only grown to meet this demand. Some offer games that teach young children in a fun, engaging way that barely feels like school, while others offer in-depth curriculums in foreign languages for students whose parents only speak one language. So what should you look for when searching for a good online learning platform?
About this course: Welcome to the Reinforcement Learning course. Here you will find out about: - foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. --- with math & batteries included - using deep neural networks for RL tasks --- also known as "the hype train" - state of the art RL algorithms --- and how to apply duct tape to them for practical problems.
A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. If you're working towards an understanding of machine learning, it's important to know how to work with decision trees. This course covers the essentials of machine learning, including predictive analytics and working with decision trees. In this course, we'll explore several popular tree algorithms and learn how to use reverse engineering to identify specific variables.
This course is about Reinforcement Learning. The first step is to talk about the mathematical background: we can use a Markov Decision Process as a model for reinforcement learning. We can solve the problem 3 ways: value-iteration, policy-iteration and Q-learning. Q-learning is a model free approach so it is state-of-the-art approach. It learns the optimal policy by interacting with the environment.