Introduction to Machine Learning for Non-Programmers

Course 1259

  • Duration: 2 days
  • Labs: Yes
  • Language: English
  • Level: Foundation

This No Code Machine Learning course provides a practical and accessible approach to utilising no code Machine Learning for data evaluation, prediction, analysis, and optimisation. 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 Delivery Methods

  • In-Person

  • Online

Introduction to Machine Learning Training Information

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 Analyse 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


Introduction to Machine Learning Training Outline

  • 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

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

Hands-On Exercise 2.1: Creating datasets for training models

  • 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

  • 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

  • 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

  • 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

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Introduction to Machine Learning FAQs

The course will use two main tools: Azure Machine Learning Designer and AWS Sagemaker Studio. These tools are designed for non-technical business analysts.

No, the pre-built pipelines and algorithms come as part of the tools. It's similar to having a pre-built chart in MS Excel, and you can change the appearance of the chart by changing the values.

Yes! The labs will be easy for a non-technical user, and you will be able to use the exercise manual immediately to start creating no-code machine-learning models with the tools described.

This course enables individuals with no technical coding skills to leverage ML and AI technologies. It empowers a wider non-technical audience to benefit from the power of ML and AI without having to rely on data scientists and data engineers.

It simplifies the development workflow by automating complex tasks and enables collaboration and communication between technical and non-technical team members. It allows stakeholders from non-technical backgrounds to collaborate in the development of machine learning models.