Artificial Intelligence De-mystified

Some people claim that AI will have negative consequences for jobs and people, others say that we should embrace AI. Either way, understanding AI is now a critical weapon in your professional skills armoury.

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But where do you start? AI is a vast and complex area of study, but AI de-mystified is designed to get you started.

AI de-mystified will enable non-experts to understand what AI is, and what it can do for you. After this course, you will understand the difference between AI, Machine Learning, and Big Data; what questions you need to be asking to whom; and what skills you and your colleagues will need to develop in order to get in front of the AI wave.
What are the learning outcomes?

At the end of this course, participants will have acquired the following:

Skills

  • recognise where AI can add value
  • select appropriate AI and machine learning models
  • relevant Maths skills.

Knowledge

  • capabilities and constraints of AI
  • how AI works, and how to build AI solutions
  • different AI models, software, languages and approaches.

Capabilities

  • frame opportunities for using AI
  • guide the development of an AI project
  • deliver value from AI projects.

Tools

  • virtual machine set up as a machine learning ‘sandbox’
  • code samples
  • use cases and models.
Course Structure

This course is broken down into 3 sections:

1. Introduction

  • Introduction to the Course
  • Hello AI
  • Getting Started
  • Working In The Sandbox
  • Legal Notices

2. What is AI?

  • Where is AI?
  • Machine Learning First Steps
  • Neural Network – First Steps 
  • Building A Fraud Detector 
  • Building a Machine That Learns 

3. Working with Data

  • Learning From Data
  • Preparing Data 
  • Data Formats
  • Working With Arrays
  • Working With DataFrames 

4. Machine Learning Applications

  • Regression 
  • Classification 
  • Credit Scoring
  • Pricing 
  • Classifying Images 
  • Classifying Words 

5. Neural Networks

  • Key Neural Network Functions
  • Essential Neural Network Mathematics
  • A Neural Network in 9 Lines of Code

6. Summary

  • What Have We Learned?
Costings

Please speak to your Pearson Representative for more information.

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