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.
Programming Statistical Applications in R is an introductory course teaching the basics of programming mathematical and statistical applications using the R language. The course makes extensive use of the Introduction to Scientific Programming and Simulation using R (spuRs) package from the Comprehensive R Archive Network (CRAN). The course is a scientific-programming foundations course and is a useful complement and precursor to the more simulation-application oriented R Programming for Simulation and Monte-Carlo Methods Udemy course. The two courses were originally developed as a two-course sequence (although they do share some exercises in common). Together, both courses provide a powerful set of unique and useful instruction about how to create your own mathematical and statistical functions and applications using R software.
The average salary of a Machine Learning Engineer in the US is $166,000! By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real life problems in your business, job or personal life with Machine Learning algorithms. Come learn Machine Learning with Python this exciting course with Anthony NG, a Senior Lecturer in Singapore who has followed Rob Percival's "project based" teaching style to bring you this hands-on course. With over 18 hours of content and more than fifty 5 star rating, it's already the longest and best rated Machine Learning course on Udemy! You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.
About this course: Welcome to Course 3 - Models & Frameworks to Support Sales Planning – In this course, you'll go through a conceptual approach to selling models and frameworks. As a primary learning outcome of this course, we emphasize the improvement in the analytical competencies and skills to develop sales planning and management. And the learning process goes through the application of the models and frameworks that contribute to supporting these processes. This course is aimed at professionals who seek improvement in conceptual support to the sales planning process, especially with an emphasis on applying selling models and frameworks methodology. At this point of the Strategic Sales Management specialization, you have an excellent understanding of the integration of sales planning to the strategy of the company.
For over 150 years, Penn Engineering's world-acclaimed faculty, state-of-the-art research laboratories and highly interdisciplinary curricula have offered a learning experience that is unparalleled. Having evolved in transformative ways to meet the technological opportunities and challenges of the 21st Century, the School's educational philosophy has remained constant: to integrate current theory and hands-on experience with modern instrumentation and analytic techniques. The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
Hoy traemos a este espacio esta infografía de ZDnet, que nos presentan así: Infographic: 50 percent of companies plan to use AI soon, but haven't worked out the details yet Despite lacking experience and skills, many respondents to a recent Tech Pro Research survey said they'd find a way to pull off the implementation in-house. In a recent survey by Tech Pro Research, only 28 percent of respondents, most of whom were in IT leadership positions, said they have firsthand experience with AI or machine learning. However, if the survey results hold true, the majority of respondents will be using the technologies at work in the next few years. Another interesting findings from this survey was that while 42 percent of respondents said their technical staff lack the skills to implement and support AI and machine learning, 41 percent said that all the work in this area would be done in-house. Thirty-nine percent of respondents said their companies were also still working on selecting AI and machine learning vendors.
Spark lets you apply machine learning techniques to data in real time, giving users immediate machine-learning based insights based on what's happening right now. Using Spark, we can create machine learning models and programs that are distributed and much faster compared to standard machine learning toolkits such as R or Python. In this course, you'll learn how to use the Spark MLlib. You'll find out about the supervised and unsupervised ML algorithms. You'll build classifications models, extracting proper futures from text using Word2Vect to achieve this.
Often, as part of exploratory data analysis, a histogram is used to understand how data are distributed, and in fact this technique can be used to compute a probability mass function (or PMF) from a data set as was shown in an earlier module. However, the binning approach has issues, including a dependance on the number and width of the bins used to compute the histogram. One approach to overcome these issues is to fit a function to the binned data, which is known as parametric estimation. Alternatively, we can construct an approximation to the data by employing a non-parametric density estimation. The most commonly used non-parametric technique is kernel density estimation (or KDE).