Bayesian Computational Analyses with R is an introductory course on the use and implementation of Bayesian modeling using R software. The Bayesian approach is an alternative to the "frequentist" approach where one simply takes a sample of data and makes inferences about the likely parameters of the population. In contrast, the Bayesian approach uses both likelihood functions and a sample of observed data (the'prior') to estimate the most likely values and distributions for the estimated population parameters (the'posterior'). The course is useful to anyone who wishes to learn about Bayesian concepts and is suited to both novice and intermediate Bayesian students and Bayesian practitioners. It is both a practical, "hands-on" course with many examples using R scripts and software, and is conceptual, as the course explains the Bayesian concepts. All materials, software, R scripts, slides, exercises and solutions are included with the course materials.

Get your team access to Udemy's top 2,000 courses anytime, anywhere. This course has two parts. Part one refers to Dummy Variable Regression and part two refers to conjoint analysis. Let me give you details of what you are going to get in each part. How to know, what kind of situation you have.

Until recently, the quality of classroom instruction relied almost entirely on a teacher's resourcefulness, motivation and intelligence. Soon, it will also depend on artificial intelligence--with lessons based more on what students need to learn than on traditional methods of instruction. "Even with PCs, projectors and the internet, the way students are taught hasn't changed that much in the past 50 years," says Rose Luckin, the chair of learning with digital technologies at University College London's Knowledge Lab. "With artificial intelligence helping the teacher, there will be a revolution in education that addresses many of teaching's shortcomings." Sometimes called "the fourth industrial revolution" (after steam, electricity and computing), artificial intelligence is all around us, from beating people at poker to analyzing mortgage applications and replying to requests on voice-activated phone systems at home or on your phone.

In this course, we will examine in detail the R software, which is the most popular statistical programming language of recent years. You will start with exploring different learning methods, clustering, classification, model evaluation methods and performance metrics. From there, you will dive into the general structure of the clustering algorithms and develop applications in the R environment by using clustering and classification algorithms for real-life problems Next, you will learn to use general definitions about artificial neural networks, and the concept of deep learning will be introduced. The elements of deep learning neural networks, types of deep learning networks, frameworks used for deep learning applications will be addressed and applications will be done with R TensorFlow package. Finally, you will dive into developing machine learning applications with SparkR, and learn to make distributed jobs on SparkR.

Marco Ribeiro, a graduate student at the University of Washington in Seattle, probes DNNs using what are called "counterfactual probes" -- a trick related to that used by the MIT researchers to spoof DNNs. The idea is to give the DNN inputs carefully varied over a wide range, and see which way the outputs of the DNN jump. For example, consider a neural network that reads in the text of movie reviews, and then flags those that give a movie a thumb's-up. To do this, the DNN would first be trained by being given reviews flagged as being positive, plus reviews flagged as being negative, with the DNN's ability then using the training from these examples to flag reviews appropriately itself.

It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your research as experienced investor. Learning machine trading analysis is indispensable for finance careers in areas such as computational finance research, computational finance development, and computational finance trading mainly within investment banks and hedge funds. It is also essential for academic careers in computational finance. And it is necessary for experienced investors computational finance trading research and development. But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500 Index ETF prices historical data for back-testing to achieve greater effectiveness.

It's too early to worry about a sentient AI apocalypse. The reality is that we know very little about how the human brain works -- which means we know even less about how to build a computer that works just like the human brain. For very specific tasks, AI tends to make rapid progress until it matches human-level performance; then progress tends to slow down. So despite fears of an AI dystopia, the technology is still very limited compared to human intelligence. A more practical problem in AI is figuring out good ways for engineers and product managers to communicate a shared vision for how to actually use AI in the enterprise.

In this course you will build MULTIPLE practical systems using natural language processing, or NLP - the branch of machine learning and data science that deals with text and speech. This course is not part of my deep learning series, so it doesn't contain any hard math - just straight up coding in Python. All the materials for this course are FREE. After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff. The first thing we'll build is a spam detector.

About this course: If you want to break into competitive data science, then this course is for you! Participating in predictive modelling competitions can help you gain practical experience, improve and harness your data modelling skills in various domains such as credit, insurance, marketing, natural language processing, sales' forecasting and computer vision to name a few. At the same time you get to do it in a competitive context against thousands of participants where each one tries to build the most predictive algorithm. Pushing each other to the limit can result in better performance and smaller prediction errors. Being able to achieve high ranks consistently can help you accelerate your career in data science.

What is machine learning / ai? How to learn machine learning in practice? Neural Networks (often referred to as deep learning) are particular interesting. But there are a few questions. To answer these questions and give beginners a guide to really understand them, I created this interesting course.