Get yourself trained on LEARNING PATH: R: with this Online Training LEARNING PATH: R: Machine Learning and Deep Learning with R.
Online Training LEARNING PATH: R: Machine Learning and Deep Learning with R
Machine learning is a subfield of computer science that gives computers the ability to learn without being explicitly programmed. Deep Learning is the next big thing and a part of machine learning. Its favorable results in applications with huge and complex data is remarkable. R is one of the most popular programming languages among the data science professionals. So, if you’re a data science professional who wants to learn machine learning and deep learning with R, then go for this Learning Path. Packts Video Learning Path is 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. The highlights of this Learning Path are: Classify data with the help of statistical methods such as k-NN Classification, logistic regression, and decision trees Learn to develop machine learning applications and distributed jobs with SparkR Dive deeper into deep learning and artificial neural networks Let’s take a quick look at your learning journey. This Learning Path starts off by explaining different learning methods such as clustering, classification, model evaluation, and performance metrics. You will then dive into the general structure of the clustering algorithms and develop applications in the R environment by using clustering and classification algorithms for real-life problems. Next, you will explore elements of deep learning neural networks, types of deep learning networks, and frameworks used for deep learning applications with building an application in TensorFlow package. You will learn to develop machine learning applications and distributed jobs with SparkR. Moving ahead, this Learning Path teaches you how to leverage deep learning to make sense of your raw data by exploring various hidden layers of data. You will understand the basics of deep learning and artificial neural networks. You will then explore advanced ANNs and RNNs. Next, you will deep dive into convolutional neural networks and unsupervised learning. Finally, you will learn about the applications of deep learning in various fields and understand the practical implementations of scalability, HPC, and feature engineering. By the end of this Learning Path, you will be able to build powerful machine learning and deep learning applications with the help of R. Meet Your Experts: We have the best works of the following esteemed authors to ensure that your learning journey is smooth: Olgun Aydin is a PhD candidate at Department of Statistics, Mimar Sinan University. He has been working on Deep Learning for his PhD thesis. He is also working as a Data Scientist.He is familiar with Big Data technologies such as Hadoop, Spark and is able to use Hive, Impala. He is a big fan of R. He loves to work with Shiny and SparkR.He has many academic papers and proceedings about applications of statistics on different disciplines. Vincenzo Lomonaco is a Deep Learning PhD student at the University of Bologna and founder of ContinuousAI .com an open source project aiming to connect people and reorganize resources in the context of Continuous Learning and AI. He is also the PhD students’ representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses Machine Learning and Computer Architectures in the same department. Previously, he was a Machine Learning software engineer at IDL in-line devices and a master student at the University of Bologna where he graduated cum laude in 2015 with the dissertation Deep Learning for Computer Vision: A comparison between CNNs and HTMs on object recognition tasks”.
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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.