Get yourself trained on Advanced Machine Learning with this Online Training Advanced Machine Learning with R.
Online Training Advanced Machine Learning with R
Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed. It explores the study and construction of algorithms that can learn from and make predictions on data. The R language is widely used among statisticians and data miners to develop statistical software and data analysis. Machine Learning is a cross-functional domain that uses concepts from statistics, math, software engineering, and more.In this course, youll get to know the advanced techniques for Machine Learning with R, such as hyper-parameter turning, deep learning, and putting your models into production through solid, real-world examples. In the first example, youll learn all about neural networks through an example of DNA classification data. Youll explore networks, implement them, and classify them.After that, youll see how to tune hyper-parameters using a data set of sonar data and youll get to know their properties. Next, youll understand unsupervised learning with an example of clustering politicians, where youll explore new patterns, understand unsupervised learning, and visualize and cluster the data.Moving on, we discuss some of the details of putting a model into a production system so you can use it as a part of a larger application. Finally, well offer some suggestions for those who wish to practice the concepts further.About the AuthorTim 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.