Get yourself trained on Data Analysis and with this Online Training Data Analysis and Machine Learning with R.
Online Training Data Analysis and Machine Learning with R
Data analysis has recently emerged as a very important focus for a huge range of organizations and businesses. Machine Learning explores the study and construction of algorithms that can learn from and make predictions on data. R makes detailed data analysis easier, making advanced data exploration and insight accessible to anyone interested in learning it. The R language is widely used among statisticians and data miners to develop statistical software and data analysis.This comprehensive 2-in-1 course follows a recipe-based approach to exploring advanced algorithm and visualization concepts to get the most out of your data through real-world examples. To begin with, youll perform analyzing techniques and learn to handle missing values and duplicates. Youll also learn to apply classification techniques and regression techniques. Moving further, youll work with advanced algorithms and techniques to enable efficient Machine Learning using the R programming language. Finally, youll work with a variety of real-world algorithms such as decision trees and support vector machines.Towards the end of this course, you’ll explore advanced algorithm and visualization concepts to get the most out of your data through real-world examples.Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, R Data Analysis Solutions – Machine Learning Techniques, covers analyzing techniques to get the most out of your data. This video empowers you by showing you ways to use R to generate professional analysis reports. It provides examples of various important analysis and machine-learning tasks that you can try out with associated and readily available data. You will learn to carry out different tasks on the data to bring it into action. By the end of this course, you will be able to carry out different analyzing techniques, apply classification and regression, and also reduce data.The second course, Machine Learning using Advanced Algorithms and Visualization in R, covers Advanced Algorithms and additional visualization. In this course, you will work through various examples of advanced algorithms and focus a bit more on some visualization options. Well start by showing you how to use the random forest to predict what type of insurance a patient has based on their treatment and you will get an overview of how to use random forest/decision tree and examine the model. Then, well walk you through the next example on letter recognition, where you will train a program to recognize letters using a support Vector machine, examine the results, and plot a confusion matrix. After that, you will look into the next example on soil classification from satellite data using K-Nearest Neighbor where you will predict what neighborhood a house is in based on other data about it. Finally, youll dive into the last example of predicting a movie genre based on its title, where you will use the tm package and learn some techniques for working with text data.Towards the end of this course, you’ll explore advanced algorithm and visualization concepts to get the most out of your data through real-world examples.About the AuthorsViswa Viswanathan is an associate professor of Computing and Decision Sciences at the Stillman School of Business in Seton Hall University. After completing his Ph.D. in Artificial Intelligence, Viswa spent a decade in Academia and then switched to a leadership position in the software industry for a decade. During this period, he worked for Infosys, Igate, and Starbase. He embraced Academia once again in 2001. Viswa has taught extensively in diverse fields, including operations research, computer science, and software engineering, management information systems, and enterprise systems. In addition to teaching at the university, Viswa has conducted training programs for industry professionals. He has written several peer-reviewed research publications in journals such as Operations Research, IEEE Software, Computers and Industrial Engineering, and International Journal of Artificial Intelligence in Education. He has authored a book entitled Data Analytics with R: A Hands-on Approach.Shanthi Viswanathan is an experienced technologist who has delivered technology management and enterprise architecture consultations to many enterprise customers. She has worked for Infosys Technologies, Oracle Corporation, and Accenture. As a consultant, Shanthi has helped several large organizations, such as Canon, Cisco, Celgene, Amway, Time Warner Cable, and GE, among others, in areas such as data architecture and analytics, master data management, service-oriented architecture, business process management, and modeling. When she is not in front of her Mac, Shanthi spends time hiking in the suburbs of NY/NJ, working in the garden, and teaching yoga. Shanthi would like to thank her husband, Viswa, for all the great discussions on numerous topics during their hikes together and for exposing her to R and Java. She would also like to thank her sons, Nitin and Siddarth, for getting her into the data analytics world.Tim Hoolihan currently works at DialogTech, a marketing analytics company focused on conversations. He is the Senior Director of Data Science there. Prior to that, he was CTO at Level Seven, a regional consulting company in the US Midwest. He is the organizer of the Cleveland R User Group. In his job, he uses deep neural networks to help automate of a lot of conversation classification problems. In addition, he works on some side-projects researching other areas of Artificial Intelligence and Machine Learning. Outside Data Science, he is interested in mathematical computation in general; he is a lifelong math learner and really enjoys applying it wherever he can. Recently, he has been spending time in financial analysis, and game development. He also knows a variety of languages: R, Python, Ruby, PHP, C/C++, and so on. Previously, he worked in web application and mobile development.
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As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.
And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.
Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.