Machine Learning Basics

Machine Learning Basics

This course is designed for anyone who wants to get started with visual machine learning in Dataiku DSS.

About this course

 
Machine Learning Basics
 

The Machine Learning Course is designed to provide a first hands-on overview of basic Dataiku DSS machine learning concepts so that you can easily create and evaluate your first models in DSS. Completion of this course will enable you to move on to more advanced courses.

In this course, we'll work with two use cases. To illustrate concepts, we'll use a Hospital Readmission project to predict whether or not a patient is likely to be readmitted to the hospital.

Then, in the hands-on lessons, we will continue with the Haiku T-Shirts project we created in Basics 101-103, and use the historical data about customers to predict whether or not a new customer will become a high revenue customer.

Before each hands-on section, you will have a chance to grasp each new concept by watching short videos. In the concept videos, we’ll explore a model that predicts whether a patient will be readmitted to a hospital, based on features such as demographics.

Learning Objectives

At the end of this course, you will be able to:

1 - Prepare a dataset for machine learning

2 - Create models using visual machine learning

3 - Evaluate and tune your models

4 - Incorporate fundamentals of Explainable AI

 

Course Properties

Course Title Machine Learning Basics

Target Audience

Anyone who uses or wants to learn how to get started with visual machine learning in Dataiku DSS

Access Level

Free / included with registration

Estimated Time for Completion

97 Minutes

Completion Criteria

Pass all the waypoint quizzes with a 65% score and the course checkpoint with an 80% score

Supplemental Materials (Y/N)

Dataset - Haiku T-Shirts Order Log

Dataset - Haiku T-Shirts Customers

Knowledge Prerequisite(s)

NONE

Technical Prerequisite(s)

Dataiku DSS - Latest version (FREE EDITION is enough 

Curriculum97 min

  • Course Introduction
  • Preview
    Course Introduction: Machine Learning Basics
  • Create the Model
  • Concept: Preparing a Dataset for Machine Learning

    Concept: Preparing a Dataset for Machine Learning

  • Concept: Quick Models

    Quick Models

  • Concept: Design Tab Overview

    Concept: Design Tab Overview

  • Concept Summary: Create the Model

    Concept Summary: Create Your First Model

  • Hands-On: Create the Model

    Hands On: Create the Model

  • Quiz: Create the Model

    Quiz: Create the Model

  • Evaluate the Model
  • Concept: Result Tab Overview
  • Concept: Model Summary Overview
  • Concept Summary: Evaluate the Model
  • Hands-On: Evaluate the Model
  • Quiz: Evaluate the Model

    Quiz: Evaluate the Model

  • Tune the Model
  • Introduction to Tuning the Model
  • Concept: Feature Handling
  • Concept: Review the Design
  • Concept: Algorithms & Hyperparameters

    Concept: Algorithms & Hyperparameters

  • Concept Summary: Tune the Model
  • Hands-On: Tune the Model
  • Quiz: Tune the Model
  • Explainable AI
  • Concept: Explainable AI
  • Concept: Partial Dependencies
  • Concept: Subpopulation Analysis
  • Concept: Individual Explanations
  • Concept Summary: Explainable AI Section
  • Hands-On: Explain Your Model
  • Quiz: Explainable AI
  • Wrap Up
  • Course Checkpoint
  • Tell Us What You Think
  • Course Complete

About this course

 
Machine Learning Basics
 

The Machine Learning Course is designed to provide a first hands-on overview of basic Dataiku DSS machine learning concepts so that you can easily create and evaluate your first models in DSS. Completion of this course will enable you to move on to more advanced courses.

In this course, we'll work with two use cases. To illustrate concepts, we'll use a Hospital Readmission project to predict whether or not a patient is likely to be readmitted to the hospital.

Then, in the hands-on lessons, we will continue with the Haiku T-Shirts project we created in Basics 101-103, and use the historical data about customers to predict whether or not a new customer will become a high revenue customer.

Before each hands-on section, you will have a chance to grasp each new concept by watching short videos. In the concept videos, we’ll explore a model that predicts whether a patient will be readmitted to a hospital, based on features such as demographics.

Learning Objectives

At the end of this course, you will be able to:

1 - Prepare a dataset for machine learning

2 - Create models using visual machine learning

3 - Evaluate and tune your models

4 - Incorporate fundamentals of Explainable AI

 

Course Properties

Course Title Machine Learning Basics

Target Audience

Anyone who uses or wants to learn how to get started with visual machine learning in Dataiku DSS

Access Level

Free / included with registration

Estimated Time for Completion

97 Minutes

Completion Criteria

Pass all the waypoint quizzes with a 65% score and the course checkpoint with an 80% score

Supplemental Materials (Y/N)

Dataset - Haiku T-Shirts Order Log

Dataset - Haiku T-Shirts Customers

Knowledge Prerequisite(s)

NONE

Technical Prerequisite(s)

Dataiku DSS - Latest version (FREE EDITION is enough 

Curriculum97 min

  • Course Introduction
  • Preview
    Course Introduction: Machine Learning Basics
  • Create the Model
  • Concept: Preparing a Dataset for Machine Learning

    Concept: Preparing a Dataset for Machine Learning

  • Concept: Quick Models

    Quick Models

  • Concept: Design Tab Overview

    Concept: Design Tab Overview

  • Concept Summary: Create the Model

    Concept Summary: Create Your First Model

  • Hands-On: Create the Model

    Hands On: Create the Model

  • Quiz: Create the Model

    Quiz: Create the Model

  • Evaluate the Model
  • Concept: Result Tab Overview
  • Concept: Model Summary Overview
  • Concept Summary: Evaluate the Model
  • Hands-On: Evaluate the Model
  • Quiz: Evaluate the Model

    Quiz: Evaluate the Model

  • Tune the Model
  • Introduction to Tuning the Model
  • Concept: Feature Handling
  • Concept: Review the Design
  • Concept: Algorithms & Hyperparameters

    Concept: Algorithms & Hyperparameters

  • Concept Summary: Tune the Model
  • Hands-On: Tune the Model
  • Quiz: Tune the Model
  • Explainable AI
  • Concept: Explainable AI
  • Concept: Partial Dependencies
  • Concept: Subpopulation Analysis
  • Concept: Individual Explanations
  • Concept Summary: Explainable AI Section
  • Hands-On: Explain Your Model
  • Quiz: Explainable AI
  • Wrap Up
  • Course Checkpoint
  • Tell Us What You Think
  • Course Complete