In this course, we will examine in detail the R software, which is
the most popular statistical programming language of recent years.
You will start with exploring different learning methods, clustering,
classification, model evaluation methods and performance metrics. From
there, you will 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 learn to use general definitions about artificial neural
networks, and the concept of deep learning will be introduced. The
elements of deep learning neural networks, types of deep learning
networks, frameworks used for deep learning applications will be
addressed and applications will be done with R TensorFlow package.
Finally, you will dive into developing machine learning applications
with SparkR, and learn to make distributed jobs on SparkR.
About the Author
Olgun is PhD candidate at
Department of Statistics, Mimar Sinan University. He has been working on
Deep Learning for his PhD thesis. Also working as Data Scientist.He is
so familiar with Big Data technologies like Hadoop, Spark and able to
use Hive, Impala. He is a big fan of R. Also he really loves to work
with Shiny, SparkR.He has many academic papers and proceedings about
applications of statistics on different disciplines. Mr. Olgun really
loves statistic and loves to investigate new methods, share his
experience with people.
