If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
About this course: Regression Analysis is perhaps the single most important Business Statistics tool used in the industry. Regression is the engine behind a multitude of data analytics applications used for many forms of forecasting and prediction. This is the fourth course in the specialization, "Business Statistics and Analysis". The course introduces you to the very important tool known as Linear Regression. You will learn to apply various procedures such as dummy variable regressions, transforming variables, and interaction effects. All these are introduced and explained using easy to understand examples in Microsoft Excel. The focus of the course is on understanding and application, rather than detailed mathematical derivations. Note: This course uses the'Data Analysis' tool box which is standard with the Windows version of Microsoft Excel. It is also standard with the 2016 or later Mac version of Excel. However, it is not standard with earlier versions of Excel for Mac.
If you want to learn how to perform the most useful statistical analyses in the R program, you have come to the right place. Now you don't have to scour the web endlessly in order to find how to do a Pearson or Spearman correlation, an independent t test or a factorial ANOVA, how to perform a sequential regression analysis or how to compute the Cronbach's alpha. Everything is here, in this course, explained visually, step by step. So, what will you learn in this course? First of all, you will learn how to perform association tests in R, both parametric and non-parametric: the Pearson correlation, the Spearman and Kendall correlation, the partial correlation and the chi-square test for independence.
I first wrote about India's tryst with MOOCs, a free, open-source online education platform that was the hottest new thing in education, five years ago. At the time, I was astonished to learn that India formed the second-largest cohort on edX, a MIT-Harvard MOOCs platform, even though the whole concept was relatively new around the globe and broadband penetration in the country was still pretty lousy. To me, this simply emphasized a recurring theme that in a land where sub-standard education abounds, any reasonable alternative stands a good chance at shining. Yet, while the appeal of online education was tremendous -- who wouldn't want to take an economics course from a Nobel Laureate for free? An executive guide to the technology and market drivers behind the $135 billion robotics market.
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 pairs trading analysis is indispensable for finance careers in areas such as quantitative research, quantitative development, and quantitative trading mainly within investment banks and hedge funds. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors quantitative trading research and development. But as learning curve can become steep as complexity grows, this course helps by leading you step by step using MSCI Countries Indexes ETF prices historical data for back-testing to achieve greater effectiveness.
In this course you will get a complete understanding of Machine Learning concepts. The industry standard best practices for formulating, applying and maintaining data driven products. It starts off with basic explanation of Machine Learning concepts and how to setup your environment. Next we take up data wrangling and EDA with Pandas. We step into Machine Learning algorithms linear and logistic regression and build real world solutions with them.
This is a "hands-on" business analytics, or data analytics course teaching how to use the popular, no-cost R software to perform dozens of data mining tasks using real data and data mining cases. It teaches critical data analysis, data mining, and predictive analytics skills, including data exploration, data visualization, and data mining skills using one of the most popular business analytics software suites used in industry and government today. The course is structured as a series of dozens of demonstrations of how to perform classification and predictive data mining tasks, including building classification trees, building and training decision trees, using random forests, linear modeling, regression, generalized linear modeling, logistic regression, and many different cluster analysis techniques. The course also trains and instructs on "best practices" for using R software, teaching and demonstrating how to install R software and RStudio, the characteristics of the basic data types and structures in R, as well as how to input data into an R session from the keyboard, from user prompts, or by importing files stored on a computer's hard drive. All software, slides, data, and R scripts that are performed in the dozens of case-based demonstration video lessons are included in the course materials so students can "take them home" and apply them to their own unique data analysis and mining cases.
About this course: This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models.
R is an open source programming language and software environment for statistical computing and graphics. R language is widely used among statisticians and data miners for developing statistical software and data analysis. R is open source and allows integration with other applications and systems. Compared to other data analysis platforms, R has an extensive set of data products. Problems faced with data like optimization and analyzation are cleared with R's excellent data visualization feature.
Decision Tree Model building is one of the most applied technique in analytics vertical. The decision tree model is quick to develop and easy to understand. The technique is simple to learn. A number of business scenarios in lending business / telecom / automobile etc. require decision tree model building. How long the course should take?