Get yourself trained on Spark for Machine with this Online Training Spark for Machine Learning.
Online Training Spark for Machine Learning
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, youll learn how to use the Spark MLlib. Youll find out about the supervised and unsupervised ML algorithms. Youll build classifications models, extracting proper futures from text using Word2Vect to achieve this. Next, well build a Logistic Regression Model with Spark. Then well find clusters and correlations in our data using K-Means clustering. Well learn how to validate models using cross-validation and area under the ROC measurement.Youll also build an effective Recommendation Model using distributed Spark algorithm. We will look at graph processing with GraphX library. By the end of the course, youll be able to focus on leveraging Spark to create fast and efficient machine learning programs.About the authorTomasz Lelek is a Software Engineer who programs mostly in Java and Scala. He is a fan of microservices architecture and functional programming. He dedicates considerable time and effort to be better every day. Recently, hes been diving into Big Data technologies such as Apache Spark and Hadoop. He is passionate about nearly everything associated with software development.Tomasz thinks that we should always try to consider different solutions and approaches before solving a problem. Recently, he was a speaker at several conferences in Poland – Confitura and JDD (Java Developers Day) and also at Krakow Scala User Group.He also conducted a live coding session at Geecon Conference.
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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.