Machine Learning Data Readiness

Course Modality

Digital training, Video

Course Level


Course Time


Course Language


Course Overview

This course focuses on the concept of data readiness in the context of machine learning (ML).You will learn how to determine data readiness and identify when to employ data readiness as part of your ML process. Using the KNIME Analytics tool, you will navigate three case studies, which cover various machine learning concepts and techniques that span basic, intermediate, and advanced levels. At the end of this course, you will be able to perform data readiness exercises across a variety of use cases.


We recommend that attendees of this course have the following prerequisites: 

  • Solutions architect background 
  •  A need to apply ML to business data (data science or ML background is not required)

Intended Audience

This course is intended for: 

  • IT or business analysts who need to employ ML algorithms to solve business problems 
  • Solutions architects and account professionals who support clients with ML needs

Why The DataTech Labs ?

Self-Paced Online Video

A 360-degree learning approach that you can adapt to your learning style

A 360-degree learning approach that you can adapt to your learning style

Engage and learn more with these live and highly-interactive classes alongside your peers

24/7 Teaching Assistance

24/7 Teaching Assistance Keep engaged with integrated teaching

Online Practice Labs

Projects provide you with sample work to show prospective employers.

Applied Projects

Real-world projects relevant to what you’re learning throughout the program

Learner Social Forums

A support team focused on helping you succeed alongside a peer community

Skill Covered

In this course, you will learn how to: 

  • Identify machine learning data readiness
  • Determine when you need to employ data readiness techniques
  • Use the KNIME Analytics tool to prepare ML models with respect to data readiness 
    Conduct data readiness exercises using beginner, intermediate, and advanced techniques

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Course Curriculum

Introduction to machine learning data readiness

Introduction to KNIME

Fundamental ML data readiness 

  •  Basic concepts and terminology 
  •  Case study

Intermediate ML data readiness 

  •  Exploring core areas of ML workflow, including: 
  •  Data reading, graphical properties, data partitioning 
  •  Training a model, applying the model, and scoring the model 
  •  Case study

Advanced ML data readiness

  •  Advanced workflow examples, including reading data, dataset evaluation,
    dimensionality reduction, and model selection 
  • Case study

Recommended Exams


AWS Certified Machine Learning – Specialty

Specialty certification is intended for individuals who perform a development or data science role. It validates a candidate's ability to design, implement, deploy, and maintain machine learning (ML) solutions for given business problems.


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