This course is all about the application of deep learning and neural networks to reinforcement learning. If you've taken my first reinforcement learning class, then you know that reinforcement learning is on the bleeding edge of what we can do with AI. Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level. Reinforcement learning has been around since the 70s but none of this has been possible until now. The world is changing at a very fast pace. The state of California is changing their regulations so that self-driving car companies can test their cars without a human in the car to supervise.

Scientists have developed an artificial intelligence system that can help predict which students are at higher risk of perpetrating school violence. The researchers found that machine learning – the science of getting computers to learn over time without human intervention – is as accurate as a team of child and adolescent psychiatrists, including a forensic psychiatrist, in determining risk for school violence. "Previous violent behaviour, impulsivity, school problems and negative attitudes were correlated with risk to others," said Drew Barzman, a child forensic psychiatrist at Cincinnati Children's Hospital Medical Center in the US. "Our risk assessments were focused on predicting any type of physical aggression at school. We did not gather outcome data to assess whether machine learning could actually help prevent school violence. That is our next goal," said Barzman, lead author of the study published in the journal Psychiatric Quarterly.

Artificial intelligence, or AI, has slowly begun to influence higher education around the world. Now, one new AI tool could change the way university students evaluate their professors. The tool is called Hubert. It is a teacher evaluation tool that appears as an AI-powered chatbot. Instead of filling out a form, students use a chat window to give feedback on the course and their professor.

About this course: Want to know how you can improve the accuracy of your machine learning models? What about how to find which data columns make the most useful features? Welcome to Feature Engineering on Google Cloud Platform where we will discuss the elements of good vs bad features and how you can preprocess and transform them for optimal use in your machine learning models. In this course you will get hands-on practice choosing features and preprocessing them inside of Google Cloud Platform with interactive labs. Our instructors will walk you through the code solutions which will also be made public for your reference as you work on your own future ML projects.

Welcome to the course on "Simple Linear Regression Analysis ( A Complete Course)" This course covers running and evaluating linear regression models (simple linear regression) including assessing the overall quality of models and interpreting individual predictors for significance with PDF files and complete exercises that consists of examples and concepts . We also explore R-Square in depth, including how to interpret R-Square for significance. Together with in-depth coverage of simple regression, we'll also explore correlation, which is closely related to regression analysis. By the end of this course you will be skilled in running and interpreting your own linear regression analyses, as well as critically evaluating the work of others. Lectures provided in HD video .While you can be confident that you are getting accurate information with Quantitative Specialists, Be confused by regression no longer -- Enroll Today!

Have you always wanted to mine twitter data? Then this course is for you. This course presents example of text mining with R. Twitter text of @pycon and @udemy is used as the data to analyze. It starts by extracting text from Twitter. The extracted text is then transformed to a corpus and then a document-term matrix.

The Vatican Secret Archives comprise 600 collections of texts spanning 12 centuries, most of which are nearly impossible to access. The Atlantic reports that a team of scientists is hoping to change that with help from some high school students and artificial intelligence software. In Codice Ratio is a new research project dedicated to analyzing the vast majority of Vatican manuscripts that have never been digitized. When other libraries wish to make a digital archive of their inventory, they often use optical-character-recognition (OCR) software. Such programs can be trained to recognize the letters in a certain alphabet, pick them out of hard-copy manuscripts, and convert them to searchable text.

In this course we will talk about the past, present and the future of AI. This course covers all the introductory topics to AI to get you started on the path of becoming AI specialist. You will learn about main philosophy, history and approaches of AI as well as its applications.

Multiple regression analysis is tool that allows you to expand on your research question, and conduct a more rigorous test of the association between your explanatory and response variable by adding additional quantitative and/or categorical explanatory variables to your linear regression model. In this session, you will apply and interpret a multiple regression analysis for a quantitative response variable, and will learn how to use confidence intervals to take into account error in estimating a population parameter. You will also learn how to account for nonlinear associations in a linear regression model. Finally, you will develop experience using regression diagnostic techniques to evaluate how well your multiple regression model predicts your observed response variable. Note that if you have not yet identified additional explanatory variables, you should choose at least one additional explanatory variable from your data set.

About this course: How can we use computers to create expressive, compelling music? And how can we write computer software to help us create and organize sounds in new ways? This course provides a hands-on introduction to the field of music technology as both a creative musical practice and an interdisciplinary technical research pursuit. Students will be able to compose music in digital audio workstation software using both audio and symbolic representations; to write code to algorithmically generate music, analyze sound, and design sound; and to describe the essential theory and history behind these activities as well as their connection to cutting-edge computer music research. Through the exploration of topics such as acoustics, psychoacoustics, digital sound, digital signal processing, audio synthesis, spectral analysis, algorithmic composition, and music information retrieval, we will explore the deep relationships between art and science, between theory and practice, and between experimental and popular electronic music.