Skills you will learn

  • Statistics in Data Mining
  • Machine Learning Techniques
  • Data Collection and Cleaning
  • Data Analysis
  • Data Interpretation

Who should learn

  • Data Science Enthusiasts
  • Beginners in R programming
  • Aspiring ML Professionals

What you will learn

  • Introduction to Data Mining Course

    • Lesson 1 - Introduction To Data Mining

      39:16
      • 1.01 Data Mining Intro Session
        09:21
      • 1.02 Data Mining Applications Phases Major Elements in Data Mining
        11:18
      • 1.03 R Tool R Studio Installation
        10:01
      • 1.04 Rapid Miner Installation Step by Step explanation
        06:16
      • 1.05 R Commands and Accessing the Tool
        02:20
    • Lesson 2 - Statistical Knowledge required for Data Mining

      17:35
      • 2.1
        08:26
      • 2.2
        09:09
    • Lesson 3 - Data Mining Concepts

      02:27:44
      • 3.01 Data Pre processing in the Data
        04:49
      • 3.02 R Studio Prelim Demo and Functional
        06:12
      • 3.03 Accessing the Datasets from R
        04:12
      • 3.04 How to remove incomplete values from the dataset with R
        03:37
      • 3.05 Plots with R
        02:28
      • 3.06 Merging and Grouping with Rapid Miner
        05:31
      • 3.07 Attribute Inclusion in the Dataset
        05:18
      • 3.08 Setting Roles in the Dataset with the RapidMiner
        05:01
      • 3.09 Handling Missing Values with Rapid Miner
        06:26
      • 3.10 Normalize and Outlier Detection with the RapidMiner
        10:56
      • 3.11 Modelling with Decision Tree with RapidMiner
        03:45
      • 3.12 Clustering With RapidMiner
        10:44
      • 3.13 K Means Clustering with Aggregation
        10:38
      • 3.14 K Means Clustering with R
        05:52
      • 3.15 Agglomerative clustering with RapidMiner
        05:07
      • 3.16 Hierarchical clustering and Visualization Techniques with R
        13:23
      • 3.17 Association Rule Mining
        11:08
      • 3.18 Association Rule Mining Support and Confidence Calculation
        05:46
      • 3.19 Apriori Algorithm
        15:59
      • 3.20 Data Exploration with R
        06:14
      • 3.21 CSV file with R
        04:38

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FAQs

  • What tools do I need to install before starting this Data Mining tutorial?

    No software installation is required for this free introductory Data Mining tutorial. Our instructor will demonstrate how to use the tools.

  • What are the ideal computer specs to perform Data Mining?

    A system with higher CPU clock speed (ex- i7 processor) and a larger capacity for storage is preferable.

  • What are the prerequisites for this free Data Mining fundamentals course?

    No prior knowledge or experience is required to take this course. However, familiarity with R programming and basic machine learning concepts would be helpful.

  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.
  • *According to Simplilearn survey conducted and subject to terms & conditions with Ernst & Young LLP (EY) as Process Advisors