Get yourself trained on LEARNING PATH: R: with this Online Training LEARNING PATH: R: Machine Learning Algorithms with R.
Online Training LEARNING PATH: R: Machine Learning Algorithms with R
Are you interested to explore advanced algorithm concepts such as random forest vector machine, K- nearest, and more through real-world examples? Then this Learning Path is for you Packts Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Machine learning and data science are some of the top buzzwords in the technical world today. Machine learning – the application and science of algorithms that makes sense of data, is the most exciting field of all the computer sciences! It explores the study and construction of algorithms that can learn from and make predictions on data. Also, R language is widely used among statisticians and data miners to develop statistical software and data analysis. We are living in an age where data comes in abundance; using the self-learning algorithms from the field of machine learning, you can turn this data into knowledge as well as gain unimaginably powerful insights into data. The highlights of this Learning Path are: Work with advanced algorithms and techniques to enable efficient machine learning using the R programming language Learn various tree based machine learning models Knowing statistics helps you build strong machine learning models that are optimized for a given problem statement. Lets take a quick look at your learning journey… Youll understand the real-world examples that demonstrates the statistical side of machine learning and familiarize you with it. In this Learning Path, youll work through various examples on advanced algorithms, and focus a bit more on some visualization options. Youll start by learning how to use 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. After that, youll explore the next example on soil classification from satellite data using K-nearest neighbor where youll predict what neighborhood a house is in – based on other data about it. Youll also dive into the example of predicting a movie genre based on its title, where youll use the tm package and learn some techniques for working with text data. Youll use libraries such as scikit-learn, e1071, randomForest, c50, xgboost, and so on. Finally, youll explore the application of frequently used algorithms on various domain problems, using both Python and R programming. By the end of the Learning Path, youll have mastered the required statistics for machine learning algorithm and will be able to apply your new skills to any sort of industry problem. Meet Your Expert: We have the best work of the following esteemed authors to ensure that your learning journey is smooth: 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. Personally, he enjoys working on practice problems on Kaggle .com as well. 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.Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, in its research and innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master’s degree from IIT Bombay in its industrial engineering and operations research program. Pratap is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies. He is also the author of the book Statistics for Machine Learning by Packt.
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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.