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 make business forecasting related decisions. Learning multiple regression analysis is indispensable for business analysis, financial analysis or data science applications in areas such as consumer analytics, finance, banking, health care, science, e-commerce and social media. It is also essential for academic careers in data science, applied statistics, economics, econometrics or quantitative finance. And it is necessary for any business forecasting related decision. But as learning curve can become steep as complexity grows, this course helps by leading you through step by step real world practical examples for greater effectiveness.

The goal of this course is to show the Swift Programmer how to build, learn and run a Neural Network using Swift. This is a minor list of what the iOS developer will learn. To see a list of everything the developer will learn please click on the curriculum link for this course.

About this course: This is the second of a two-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through use of simple conjugate models. Real-world data often require more sophisticated models to reach realistic conclusions. This course aims to expand our "Bayesian toolbox" with more general models, and computational techniques to fit them. In particular, we will introduce Markov chain Monte Carlo (MCMC) methods, which allow sampling from posterior distributions that have no analytical solution.

Are you interested in learning about R Programming? Are you interested in learning about data analysis and machine learning, but don't know where to start? Are you interested in sports and curious to know how analytics can be applied to sports? In the game of football, are you curious to know which positions are the most important? If so, you've come to the right course!

Applied Multivariate Analysis (MVA) with R is a practical, conceptual and applied "hands-on" course that teaches students how to perform various specific MVA tasks using real data sets and R software. It is an excellent and practical background course for anyone engaged with educational or professional tasks and responsibilities in the fields of data mining or predictive analytics, statistical or quantitative modeling (including linear, GLM and/or non-linear modeling, covariance-based Structural Equation Modeling (SEM) specification and estimation, and/or variance-based PLS Path Model specification and estimation. Students learn all about the nature of multivariate data and multivariate analysis. Students specifically learn how to create and estimate: covariance and correlation matrices; Principal Components Analyses (PCA); Multidimensional Scaling (MDS); Cluster Analysis; Exploratory Factor Analyses (EFA); and SEM model estimation. The course also teaches how to create dozens of different dazzling 2D and 3D multivariate data visualizations using R software.

This article is an introductory tutorial to deploy keras models with NNVM. For us to begin with, keras should be installed. Tensorflow is also required since it's used as the default backend of keras. A quick solution is to install via pip pip install -U keras --user pip install -U tensorflow --user or please refer to official site https://keras.io/#installation We load a pretrained resnet-50 classification model provided by keras.

Artificial intelligence is hitting universities, but it doesn't mean professors are being replaced by computers. "So when we talk about AI, we imagine robots, we imagine science fiction, we imagine Skynet overthrowing the world. These are the things that we imagine, but the reality is that it's not nearly that sexy," said Kyle Bowen, the educational technology services director at Penn State, during EdSurge Live's town hall on AI. "The reality is that some of the really interesting applications of this are people and computers working together to think about or to explore different problems or ideas," Bowen added. Much like Microsoft's Anthony Salcito, Bowen and other higher education influencers touted AI's ability to make data analytics and student success initiatives even easier by drawing out the most actionable data. Here are some key takeaways from EdSurge Live's two-part video series: Candace Thille, an assistant professor of education at Stanford Graduate School of Education, told EdSurge viewers that data pulled from student work should be used to craft personalized learning experiences.

The government's'Nudge Unit' is experimenting with using machine learning algorithms to rate how well schools and doctors' surgeries are performing. For the last year, The Behavioural Insights Team (BIT) has been trialling machine learning models that can crunch through publicly available data to help automate some of the decisions made by bodies such as Ofsted, which inspects schools, and the Care Quality Commission, which regulates health and social care in England. Michael Sanders, head of research at the BIT says it is working with Ofsted to put the technology into use during 2018. "We're working with them to feed into variations on our model and to improve it using additional data that they have that isn't public," he says. The school-evaluating algorithm pulls together data from a large number of sources to decide whether a school is potentially performing inadequately. It is said the system can help to identify more schools that are inadequate, when compared to random inspections.

Like many people, I feel apprehension whenever I hear or read the phrase "artificial intelligence." The expression often evokes images of the Terminator coming to exterminate people -- or take their jobs. However, firsthand experience has taught me there's nothing artificial about intelligence. In practice, AI is an extension of human intelligence that's guided by people. Far from spelling your doom, AI is more likely to save your life.

If you asked a room of teacher if they would rather work, play, or participate in professional development, professional development would likely come in last. Often, people imagine being stuck in classrooms listening to a lecture or chained to a computer for e-learning courses. And while these tried-and-true methods for professional development aren't going anywhere, there are methods for increasing engagement amongst the teachers in attendance. Understanding Gamification Gamification involves bringing elements traditionally associated with video games into the learning environment.