Deep Learning with R

4 Hours
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35 Lessons (4h)

  • Introduction to Machine Learning
    The Course Overview4:45
    Supervised and Unsupervised Learning6:13
    Feature Selection2:39
    Model Evaluation Methods - Cross Validation3:17
    Performance Metrics3:39
  • Clustering
    K-Means Clustering6:46
    Hierarchical Clustering5:36
    DBSCAN Algorithm4:09
    Clustering Exercises with R6:33
    Dealing with Problems About Clustering4:26
  • Classification
    k-NN Classification7:25
    Logistic Regression5:06
    Naive Bayes3:02
    Decision Trees3:20
    Classification Exercises with R4:04
    Handling Problems About Classification4:32
  • Artificial Neural Networks
    Introduction to Artificial Neural Networks4:27
    Types of Artificial Neural Networks3:11
    Back Propagation3:06
    Artificial Neural Networks Exercises with R3:43
    Tricks for ANN in R2:52
  • Introduction to Deep Learning
    What Is Deep Learning?5:25
    Elements of Deep Neural Networks2:26
    Types of Deep Neural Networks1:24
    Introduction to Deep Learning Frameworks4:28
    Exercises with TensorFlow in R8:01
    Tricks About Application of Deep Neural Nets1:54
  • Machine Learning with SparkR
    Introduction to SparkR1:07
    Installation of SparkR3:11
    Writing First Script on SparkR2:18
    Generalized Linear Models with SparkR3:36
    Classification Exercises with SparkR1:49
    Clustering Exercises with SparkR2:50
    Naive Bayes with SparkR1:21
    Tricks About SparkR2:25

Build Powerful Machine Learning & Deep Learning Applications with the R Programming Language

Packt Publishing

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.


In this course, you'll examine in detail the R programming language, the most popular statistical programming language in the world today. You'll start by exploring different learning methods, clustering, classification, model evaluation methods and performance metrics. As you progress to more advanced subjects, you'll develop the skills necessary to perform a variety of tasks with R.

  • Access 35 lectures & 4 hours of content 24/7
  • Delve into the general structure of clustering algorithms
  • Develop applications in the R environment by using clustering & classification algorithms for real-life problems
  • Use general definitions about artificial neural networks
  • Explore the elements of deep learning neural networks & other types of deep learning networks
  • Dive into developing machine learning algorithms w/ SparkR


Details & Requirements

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels


  • Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.
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