Introduction to Machine Learning for Non-Programmers
Course 1259
2 DAY COURSE

Price: $1,427.00
Course Outline

This No Code Machine Learning course provides a practical and accessible approach to utilizing no code Machine Learning for data evaluation, prediction, analysis, and optimization. Designed for both non-technical and technical data users, it equips you with foundational knowledge to enhance collaboration between business analysts, data scientists, and data engineers.

Introduction to Machine Learning for Non-Programmers Benefits

  • In this course, you will learn how to:

    • Create No Code Machine Learning Models: You'll learn to create common no code Machine Learning models using user-friendly, industry-standard, drag-and-drop tools.
    • Prepare and Analyze Data: Understand how to prepare and explore data to be used with Machine Learning models effectively.
    • Select Pre-built Pipelines and Algorithms: Discover how to choose pre-built pipelines and algorithms to train your Machine Learning models.
    • Explore Ready-to-Use Models: Explore ready-to-use models for tasks like natural language processing and computer vision.
    • Clustering and Regression Models: Learn to group items into clusters using a no-code Clustering Model and predict numeric values using a no-code Regression Model.
    • Classification Models: Master the art of predicting item categories using a no-code Classification Model.
  • Training Prerequisites

    None.

Introduction to Machine Learning Training Outline

Chapter 1: Overview of No Code Machine Learning 

  • What is Machine Learning?
  • What is No Code Machine Learning?
  • Why is No Code Machine Learning so important?
  • How do No Code Machine Learning Platforms work?
  • No Code Machine Learning with Microsoft Azure
  • No Code Machine Learning with Amazon AWS

Hands-On Exercise 1.1: Exploring industry-standard, visual, drag-and-drop and point-and-click Machine Learning tools

Chapter 2: Creating Datasets for Training Models 

  • Overview of datasets for Machine Learning
  • Selecting appropriate datasets
  • Preparing, exploring, and analyzing data

Hands-On Exercise 2.1: Creating datasets for training models

Chapter 3: Machine Learning models, Pre-built Pipelines, and Algorithms  

  • What is a Machine Learning model?
  • What are ready-to-use Machine Learning models?
  • Common ready-to-use Machine Learning models

Hands-On Exercise 3.1: Explore ready-to-use models for natural language processing and computer vision use cases

Chapter 4: No Code Machine Learning Clustering Models 

  • What is Clustering in Machine Learning?
  • Common use cases for Clustering
  • Clustering Machine Learning Models
  • Creating a No Code Clustering Model

Hands-On Exercise 4.1: Group items into clusters based on features and characteristics using a no-code Clustering Model

Chapter 5: No Code Machine Learning Regression Models 

  • What is Regression in Machine Learning?
  • Common use cases for Regression
  • Regression Machine Learning Models
  • Creating a Regression Machine Learning Model

Hands-On Exercise 5.1: Train a no code Regression Model to predict numeric values

Chapter 6: No Code Machine Learning Classification Models

  • What is Classification in Machine Learning?
  • Common Use Cases for Classification
  • Classification Machine Learning Models
  • Creating a Classification Machine Learning Model

Hands-On Exercise 6.1: Predict which category, or class, an item belongs to using a no-code Classification Model

Course Dates
Attendance Method

How will you be attending the class?

Selecting 'Live Virtual' allows you to attend remotely from work or home. You will receive email communication well before the class starts with detailed instructions on how to validate your equipment and connect to the classroom for a quality learning experience.

Additional Details (optional)

Private Team Training

Interested in this course for your team? Please complete and submit the form below and we will contact you to discuss your needs and budget.