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Learning Path: R: Real-World Data Mining With R

Video Description

Learn data mining with R using real-world dataset analysis techniques

In Detail

Data mining is a growing demand on the market as the world is generating data at an increasing pace. R is a popular programming language for statistics. It can be used for day-to-day data analysis tasks.

This Learning Path is the complete learning process for data-happy people. We begin with a thorough introduction to data mining and how R makes it easy with its many packages. We then move on to exploring data mining techniques, showing you how to apply different mining concepts to various statistical and data applications in a wide range of fields using R’s vast set of algorithms.

Discover the versatility of R for data mining with the collection of analysis techniques in this Learning Path.

Prerequisites: Requires basic knowledge of R

Resources: Code downloads and errata:

  • Learning Data Mining with R

  • R Data Mining Projects

  • Advanced Data Mining projects with R

  • PATH PRODUCTS

    This path navigates across the following products (in sequential order):

  • Learning Data Mining with R (2h 17m)

  • R Data Mining Projects (3h 19m)

  • Advanced Data Mining projects with R (1h 24m)

  • Table of Contents

    1. Chapter 1 : Learning Data Mining with R
      1. The Course Overview 00:03:31
      2. Getting Started with R 00:05:06
      3. Data Preparation and Data Cleansing 00:04:10
      4. The Basic Concepts of R 00:05:46
      5. Data Frames and Data Manipulation 00:05:29
      6. Data Points and Distances in a Multidimensional Vector Space 00:03:59
      7. An Algorithmic Approach to Find Hidden Patterns in Data 00:06:24
      8. A Real-world Life Science Example 00:04:29
      9. Example – Using a Single Line of Code in R 00:04:00
      10. R Data Types 00:05:44
      11. R Functions and Indexing 00:04:15
      12. S3 Versus S4 – Object-oriented Programming in R 00:04:45
      13. Market Basket Analysis 00:03:01
      14. Introduction to Graphs 00:02:09
      15. Different Association Types 00:05:27
      16. The Apriori Algorithm 00:06:38
      17. The Eclat Algorithm 00:03:54
      18. The FP-Growth Algorithm 00:03:48
      19. Mathematical Foundations 00:06:01
      20. The Naive Bayes Classifier 00:03:50
      21. Spam Classification with Naïve Bayes 00:03:33
      22. Support Vector Machines 00:04:29
      23. K-nearest Neighbors 00:03:21
      24. Hierarchical Clustering 00:05:45
      25. Distribution-based Clustering 00:06:55
      26. Density-based Clustering 00:03:12
      27. Using DBSCAN to Cluster Flowers Based on Spatial Properties 00:02:25
      28. Introduction to Neural Networks and Deep Learning 00:06:09
      29. Using the H2O Deep Learning Framework 00:02:28
      30. Real-time Cloud Based IoT Sensor Data Analysis 00:06:17
    2. Chapter 2 : R Data Mining Projects
      1. The Course Overview 00:03:53
      2. What Is Data Mining? 00:04:58
      3. Introduction to the R Programming Language 00:14:44
      4. Data Type Conversion 00:02:11
      5. Sorting, Merging, Indexing, and Subsetting Dataframes 00:09:46
      6. Date and Time Formatting 00:03:02
      7. Types of Functions 00:02:24
      8. Loop Concepts 00:02:30
      9. Applying Concepts 00:03:18
      10. String Manipulation 00:02:15
      11. NA and Missing Value Management and Imputation Techniques 00:02:52
      12. Univariate Data Analysis 00:09:19
      13. Bivariate Analysis 00:01:49
      14. Multivariate Analysis 00:00:58
      15. Understanding Distributions and Transformation 00:04:54
      16. Interpreting Distributions and Variable Binning 00:05:15
      17. Contingency Tables, Bivariate Statistics, and Checking for Data Normality 00:06:17
      18. Hypothesis Testing 00:11:59
      19. Non-Parametric Methods 00:02:37
      20. Introduction to Data Visualization 00:16:07
      21. Visualizing Charts, and Geo Mapping 00:03:39
      22. Visualizing Scatterplot, Word Cloud and More 00:10:51
      23. Using plotly 00:04:50
      24. Creating Geo Mapping 00:02:21
      25. Introduction about Regression 00:04:09
      26. Linear Regression 00:14:04
      27. Stepwise Regression Method for Variable Selection 00:02:20
      28. Logistic Regression 00:09:39
      29. Cubic Regression 00:08:47
      30. Introduction to Market Basket Analysis 00:12:29
      31. Practical project 00:15:39
    3. Chapter 3 : Advanced Data Mining projects with R
      1. The Course Overview 00:03:53
      2. Understanding Customer Segmentation 00:03:50
      3. Clustering Methods – K means and Hierarchical 00:15:37
      4. Clustering Methods – Model Based, Other and Comparison 00:05:33
      5. What Is Recommendation? 00:07:29
      6. Application of Methods and Limitations of Collaborative Filtering 00:02:31
      7. Practical Project 00:04:41
      8. Why Dimensionality Reduction? 00:09:14
      9. Practical Project around Dimensionality Reduction 00:12:41
      10. Parametric Approach to Dimension Reduction 00:02:42
      11. Introduction to Neural Networks 00:04:07
      12. Understanding the Math Behind the Neural Network 00:01:59
      13. Neural Network Implementation in R 00:01:59
      14. Neural Networks for Prediction 00:03:25
      15. Neural Networks for Classification 00:01:32
      16. Neural Networks for Forecasting 00:01:16
      17. Merits and Demerits of Neural Networks 00:02:21