Introduction to Machine Learning for Non-Programmers

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

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 analysing 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

Commercial Pricing

£1,695

2-Day Instructor-Led Training
Hands-On Labs
After-Course Instructor Coaching Included

Would you like to take this course as a team?

Include dates with afternoon start times

Mar 9 - 10 1:00 PM - 8:30 PM GMT

Virtual
Mar 16 - 17 9:00 AM - 4:30 PM GMT

London or Virtual
May 4 - 5 2:00 PM - 9:30 PM BST

Herndon, VA or Virtual
Jun 29 - 30 2:00 PM - 9:30 PM BST

Herndon, VA or Virtual
Aug 4 - 5 2:00 PM - 9:30 PM BST

Herndon, VA or Virtual
Sep 7 - 8 9:00 AM - 4:30 PM BST

London or Virtual
Nov 12 - 13 2:00 PM - 9:30 PM GMT

Herndon, VA or Virtual
Jan 4 - 5 2:00 PM - 9:30 PM GMT

Herndon, VA or Virtual

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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.

Introduction to Machine Learning for Non-Programmers

Introduction to Machine Learning Delivery Methods

Introduction to Machine Learning Training Information

Introduction to Machine Learning Training Outline

Chapter 1: Overview of No Code Machine Learning

Chapter 2: Creating Datasets for Training Models

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

Chapter 4: No Code Machine Learning Clustering Models

Chapter 5: No Code Machine Learning Regression Models

Chapter 6: No Code Machine Learning Classification Models

Live Instructor £1,695

Introduction to Machine Learning FAQs

What is the drag-and-drop technology, and point-and-click tools you will be using throughout this course?

Will we be writing the pipelines and algorithms in the class?

I am not technical, and not familiar with the tools mentioned, will I still be able to do the hands-on portion of this course?

Why is Machine Learning important to me, as a non-technical person?

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