Applied Machine Learning and Deep Learning with R

Applied Machine Learning and Deep Learning with R

Learn to build powerful machine learning and deep learning applications with help of the R programming language and its various packages

Bestseller
Created By: Olgun Aydin
15.87 9.52

About This Course

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.

Other Information

  • Certificate will provided in this course on Completion
  • Full lifetime access
  • Available on Mobile & Laptop

What Students Will Learn In Your Course?

  • Classify data with the help of statistical methods such as k-NN Classification, Logistic Regression, and Decision Trees
  • Deal with imbalanced datasets in artificial neural networks
  • Deep learning algorithms Tensorflow background in R
  • Write machine learning scripts with SparkR

Are There Any Course Requirements Or Prerequisites?

Experience in data analysis, business analysis, statistics.

Who Are Your Target Students?

This course is designed for data analysts, big data enthusiasts, business analysts, business intelligent specialists, statisticians, econometricians and for everyone interested in data analysis and data science

Course Content

  • 35 lectures
  • 02:15:06
  • The Course Overview
    00:04:45
  • Supervised and Unsupervised Learning
    00:06:13
  • Feature Selection
    00:02:39
  • Model Evaluation Methods - Cross Validation
    00:03:17
  • Performance Metrics
    00:03:39
  • K-Means Clustering
    00:06:46
  • Hierarchical Clustering
    00:05:36
  • DBSCAN Algorithm
    00:04:09
  • Clustering Exercises with R
    00:06:33
  • Dealing with Problems About Clustering
    00:04:26
  • k-NN Classification
    00:07:25
  • Logistic Regression
    00:05:06
  • Naive Bayes
    00:03:02
  • Decision Trees
    00:03:20
  • Classification Exercises with R
    00:04:04
  • Handling Problems About Classification
    00:04:32
  • Introduction to Artificial Neural Networks
    00:04:27
  • Types of Artificial Neural Networks
    00:03:11
  • Back Propagation
    00:03:06
  • Artificial Neural Networks Exercises with R
    00:03:43
  • Tricks for ANN in R
    00:02:52
  • What Is Deep Learning?
    00:05:25
  • Elements of Deep Neural Networks
    00:02:26
  • Types of Deep Neural Networks
    00:01:24
  • Introduction to Deep Learning Frameworks
    00:04:28
  • Exercises with TensorFlow in R
    00:08:01
  • Tricks About Application of Deep Neural Nets
    00:01:54
  • Introduction to SparkR
    00:01:07
  • Installation of SparkR
    00:03:11
  • Writing First Script on SparkR
    00:02:18
  • Generalized Linear Models with SparkR
    00:03:36
  • Classification Exercises with SparkR
    00:01:49
  • Clustering Exercises with SparkR
    00:02:50
  • Naive Bayes with SparkR
    00:01:21
  • Tricks About SparkR
    00:02:25
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Packt Publication

  • 4.48 (16)
  • 13 Reviews
  • 16 Students
  • 935 Courses