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Introduction to Machine Learning for Non-Programmers

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 - North America
Course Dates - Europe
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