Goto

Collaborating Authors

Practical Reinforcement Learning Coursera

#artificialintelligence

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.


Fundamentals of Decision Trees in Machine Learning

@machinelearnbot

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.


Artificial Intelligence IV - Reinforcement Learning in Java

@machinelearnbot

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.


Bolster continuous learning at your company with this system

Mashable

TL;DR: Create training modules for your employees with the Coassemble Learning Management System, on sale for $89.99 as of March 25. If you're looking for a way to bolster the continuous learning environment at your company, turn to the Coassemble Learning Management System. Great for founders, managers, and HR professionals, this LMS lets you create engaging courses for your employees and share in-house business knowledge with your team, all from a single platform. Coassemble features a user-friendly environment with flexibility and scalability, allowing your team to create unlimited courses that are tailored to your brand's training needs. You can choose from over 40 different customizable, interactive, drag and drop templates, plus you can add logos, custom fonts, and color schemes to fit your brand and update and view content on any device at any time.


IBM Machine Learning

#artificialintelligence

Offered by IBM. Machine Learning is one of the most in-demand skills for jobs related to modern AI applications, a field in which hiring has grown 74% annually for the last four years (LinkedIn). This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Machine Learning and leverage the main types of Machine Learning: Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning. It also complements your learning with special topics, including Time Series Analysis and Survival Analysis. This program consists of 6 courses providing you with solid theoretical understanding and considerable practice of the main algorithms, uses, and best practices related to Machine Learning . You will follow along and code your own projects using some of the most relevant open source frameworks and libraries. Although it is recommended that you have some background in Python programming, statistics, and linear algebra, this intermediate series is suitable for anyone who has some computer skills, interest in leveraging data, and a passion for self-learning. We start small, provide a solid theoretical background and code-along labs and demos, and build up to more complex topics. In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Machine Learning.