RapidMiner Professional

Who Should Attend?

DATA SCIENTIST & BIG DATA TEAM

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Duration: 4 Days

This training is a combinations of 3 courses that make up the RapidMiner Professional set:

1. Applications & Use Cases Professional

This course is primarily conceptual. It is an introduction to RapidMiner that provides a high-level overview to help people understand how to drive value. The course should be appropriate either for people that will use RapidMiner, or those that manage and provide oversight. Participants should be able to perform very basic tasks in RapidMiner such as simple data visualization.

2. Data Engineering Professional

This course is designed to be in-depth hands-on training in RapidMiner. The course is appropriate for anyone that will be using RapidMiner and need to be able to perform data preparation at an intermediate level.

3. Machine Learning Professional

This course is a mix of conceptual and hands-on. This course is designed for anyone that will build Machine Learning models in RapidMiner.

  1. Applications & Use Cases Professional
    • Understand the concepts of ML, DS, and related disciplines

    • Understand and apply CRISP-DM to machine learning problems

       

    • Identify and map use cases to machine learning problems

       

    • Use data visualization tools in RapidMiner to interpret data

       

    • Understand an ML model, its strengths and limitations, and how to apply it to improve business outcomes

  2. Data Engineering Professional
    • Access Data

       

    • Complete Common Data Preparation tasks

    • Perform Joins & Set Operations

       

    • Transform Data with Pivots & Aggregations

       

    • Be Able to Leverage Process Control

       

    • Understand How to Handle Text

  3. Machine Learning Professional
    • Classification and Regression

       

    • Split Validation

       

    • Scoring

       

    • Correlations

       

    • Feature Importance

       

    • Clustering and Association Analysis

Applications & Use Cases Professional

1. Basics of AI, Data Science and Machine Learning

  • Identifying Applications & Use Cases
    • Definition of terms
    • Types of Machine Learning
    • Understanding Use Cases of Machine Learning and Artificial Intelligence
  • Methodologies and Governance
    • General Guidelines
    • Building a Program
  • Running a Project

2. Data Understanding and Data Preparation

  • Data Understanding
    • Types of data
    • Accessing data
    • Visualizing data
  • Data Preparation
    • Preparing data with Turbo Prep
    • Building data prep processes

3. Model Selection, Evaluation, and Validation

  • Validating Machine Learning Models
    • Concepts for model fit
    • Split Validation
  • Model Evaluation and Selection
    • Common performance measurements
    • Model selection

4. Deployment

  • Definition of Deployment
  • How to deploy a validated model
  • Challenges

 

Data Engineering Professional

1. Access Data

  • Getting Data into RapidMiner
  • Initial Data Concerns

2. Routines

  • Subprocess
  • Building Blocks
  • Execute Process

3. Data Preparation

  • Filter Examples
  • Map or Replace
  • Replace Missing
  • Data Types
  • Generate Attributes
  • Set Role
  • Select Attributes

4. Joins & Set Operations

  • Preparing for Joins
  • Joins & Append
  • Set Operations

5. Transformation

  • Aggregate & Pivot
  • Generate Aggregation
  • Rename by Replacing
  • Loop Attributes & Generate Attributes

6. Tips & Tricks

  • Zoom, Autowire, Breakpoints
  • Advanced Parameters and Compatibility
  • Extensions
  • Organize Nodes and AutoMultiply
  • Operator Searching and Replacing
  • Wisdom of Crowds
  • XML View

7. Text Processing

  • Introduction to Text Mining
  • Use Cases
  • Text Preparation Process
  • Simple Text Processing
  • TFIDF
  • Text Processing Extension

 

Machine Learning Professional 

1. Introduction to Machine Learning

  • Introduction
  • k-NN
  • Model Validation
  • Normalize & Group Models

2. Supervised Learning

  • Regression: Linear, Logistic, GLM
  • Naïve Bayes
  • Decision Tree
  • Neural Networks

3. Deployment & Scoring

  • Deployment
  • Scoring

4. Unsupervised Learning

  • Attribute Correlations
  • Clustering
  • Association Mining

5. Feature Engineering

  • Introduction to Feature Engineering
  • Feature Weighting

6. Auto Model

  • Clustering
  • Supervised Learning
  • Deployment

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Drop us your entry if you are interested to join this course.