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Learning Data Mining with R

Video Description

A complete course to help you learn all the relevant aspects of data mining using R

About This Video

  • Use powerful R libraries to effectively get the most out of your datat

  • Gain a good level of knowledge and an understanding of the data mining disciplines to solve real-world challenges in R

  • This hands-on tutorial covers topics in three dimensions: the mathematical foundations, the actual implementation in R, and practical examples

  • 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.

    Data mining is a very broad topic and takes some time to learn. This course will help you to understand the mathematical basics quickly, and then you can directly apply what you’ve learned in R. This course covers each and every aspect of data mining in order to prepare you for real-world problems. You'll come to understand the different disciplines in data mining. In every discipline, there exist a variety of different algorithms. At least one algorithm of the various classes of algorithms will be covered to give you a foundation to further apply your knowledge to dive deeper into the different flavors of algorithms.

    After completing this course, you will be able to solve real-world data mining problems.

    Table of Contents

    1. Chapter 1 : Getting Started – A Motivating Example
      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
    2. Chapter 2 : Clustering – A Dating App for Your Data Points
      1. Data Points and Distances in a Multidimensional Vector Space 00:03:59
      2. An Algorithmic Approach to Find Hidden Patterns in Data 00:06:24
      3. A Real-world Life Science Example 00:04:29
    3. Chapter 3 : R Deep Dive, Why Is R Really Cool?
      1. Example – Using a Single Line of Code in R 00:04:00
      2. R Data Types 00:05:44
      3. R Functions and Indexing 00:04:15
      4. S3 Versus S4 – Object-oriented Programming in R 00:04:45
    4. Chapter 4 : Association Rule Mining
      1. Market Basket Analysis 00:03:01
      2. Introduction to Graphs 00:02:09
      3. Different Association Types 00:05:27
      4. The Apriori Algorithm 00:06:38
      5. The Eclat Algorithm 00:03:54
      6. The FP-Growth Algorithm 00:03:48
    5. Chapter 5 : Classification
      1. Mathematical Foundations 00:06:01
      2. The Naive Bayes Classifier 00:03:50
      3. Spam Classification with Naïve Bayes 00:03:33
      4. Support Vector Machines 00:04:29
      5. K-nearest Neighbors 00:03:21
    6. Chapter 6 : Clustering
      1. Hierarchical Clustering 00:05:45
      2. Distribution-based Clustering 00:06:55
      3. Density-based Clustering 00:03:12
      4. Using DBSCAN to Cluster Flowers Based on Spatial Properties 00:02:25
    7. Chapter 7 : Cognitive Computing and Artificial Intelligence in Data Mining
      1. Introduction to Neural Networks and Deep Learning 00:06:09
      2. Using the H2O Deep Learning Framework 00:02:28
      3. Real-time Cloud Based IoT Sensor Data Analysis 00:06:17