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Applied Machine Learning and Deep Learning with R

Video Description

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

About This Video

  • Real world, practical content that gets you up and running to take on the challenges faced by an everyday data scientist
  • Leverage the power of R programming language and its packages for powerful data science application development
  • Machine learning and deep learning made simple, one step at a time!

In Detail

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.

Table of Contents

  1. Chapter 1 : Introduction to Machine Learning
    1. The Course Overview 00:04:46
    2. Supervised and Unsupervised Learning 00:06:13
    3. Feature Selection 00:02:39
    4. Model Evaluation Methods - Cross Validation 00:03:17
    5. Performance Metrics 00:03:40
  2. Chapter 2 : Clustering
    1. K-Means Clustering 00:06:47
    2. Hierarchical Clustering 00:05:36
    3. DBSCAN Algorithm 00:04:10
    4. Clustering Exercises with R 00:06:33
    5. Dealing with Problems About Clustering 00:04:26
  3. Chapter 3 : Classification
    1. k-NN Classification 00:07:25
    2. Logistic Regression 00:05:06
    3. Naive Bayes 00:03:03
    4. Decision Trees 00:03:21
    5. Classification Exercises with R 00:04:05
    6. Handling Problems About Classification 00:04:33
  4. Chapter 4 : Artificial Neural Networks
    1. Introduction to Artificial Neural Networks 00:04:28
    2. Types of Artificial Neural Networks 00:03:12
    3. Back Propagation 00:03:06
    4. Artificial Neural Networks Exercises with R 00:03:44
    5. Tricks for ANN in R 00:02:53
  5. Chapter 5 : Introduction to Deep Learning
    1. What Is Deep Learning? 00:05:25
    2. Elements of Deep Neural Networks 00:02:27
    3. Types of Deep Neural Networks 00:01:25
    4. Introduction to Deep Learning Frameworks 00:04:29
    5. Exercises with TensorFlow in R 00:08:01
    6. Tricks About Application of Deep Neural Nets 00:01:54
  6. Chapter 6 : Machine Learning with SparkR
    1. Introduction to SparkR 00:01:07
    2. Installation of SparkR 00:03:12
    3. Writing First Script on SparkR 00:02:19
    4. Generalized Linear Models with SparkR 00:03:36
    5. Classification Exercises with SparkR 00:01:49
    6. Clustering Exercises with SparkR 00:02:50
    7. Naive Bayes with SparkR 00:01:22
    8. Tricks About SparkR 00:02:26