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.

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.

A high-level course of AI to learn how Machine Learning provides the foundation for AI, and how you can leverage cognitive services in your apps. Artificial Intelligence will define the next generation of software solutions. This computer science course provides an overview of AI, and explains how it can be used to build smart apps that help organizations be more efficient and enrich people's lives. It uses a mix of engaging lectures and hands-on activities to help you take your first steps in the exciting field of AI. Discover how machine learning can be used to build predictive models for AI.

This course is designed to make you an expert in AWS Machine Learning and it teaches you how to convert your cool ideas into highly scalable products in a matter of days. Biggest challenge for a Data Science professional is how to convert the proof-of-concept models into actual products that your customers can use. There are several courses on machine learning that teach you how to build models in R, Python, Matlab and so forth. However, converting a model into a scalable solution and integrating with your existing application requires a lot of effort and development. The real success of your ideas and concepts depends on how soon you can put the capabilities in the hands of your customers.

With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering.