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

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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.

Real World 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

For course questions or any customer service inquiry, please contact your Customer Service team at BAHCustomerService@LearningTree.com.

We are excited that Learning Tree now offers a deferred direct bill payment option for Booz Allen employees. The deferred direct bill payment option enables employees to enroll in learning opportunities with no upfront costs. This payment option will require the employee to sign a Family Educational Rights and Privacy Act (FERPA) agreement with Learning Tree to release grades/completion to Booz Allen to satisfy the FlexEd Program completion requirement. Note, Learning Tree may also be used for the FlexEd Program reimbursement payment option.

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