01/23/2023 - 01/24/2023
10:00 AM - 06:00 PM
Online Virtual Class
USD $1,395.00
Enroll
03/06/2023 - 03/07/2023
10:00 AM - 06:00 PM
Online Virtual Class
USD $1,395.00
Enroll
04/24/2023 - 04/25/2023
10:00 AM - 06:00 PM
Online Virtual Class
USD $1,395.00
Enroll

Objectives

The goals for the course are very practical:

  • Introduce managers to AI concepts
  • Introduce managers to machine learning and neural networks
  • Understand challenges in delivering AI products
  • Get a framework for better AI roles and practices

Audience

Project Managers, Business Analysts, Managers, Directors

Prerequisites

The course Managing Data-Driven Projects is recommended

Duration

Two days

Outline for Discovering and Delivering Artificial Intelligence Products Training

Chapter 1. What is "Artificial Intelligence"?        

  • The history of AI
  • Defining General Intelligence
  • "Strong" vs. "weak" AI
  • Planning AI

Chapter 2. The Rise of Machine Learning        

  • Machine Learning
  • Artificial Neural Networks
  • Perceptrons

Chapter 3. Finding the right approach        

  • Matching Patterns
  • Data vs Reasoning

Chapter 4. Common AI Products            

  • Robotics
  • Natural Language Processing
  • The Internet of Things

Chapter 5. Mixing with other technology            

  • Big Data
  • Data Science

Chapter 6. What is "machine learning"?        

  • What is means to learn
  • Working with data
  • Apply machine learning
  • Different types of learning

Chapter 7. Different ways a machine learns        

  • Supervised machine learning
  • Unsupervised machine learning
  • Semi-Supervised machine learning
  • Reinforcement learning

Chapter 8. Popular machine learning algorithms        

  • Common problems that use machine learning
  • Decision trees
  • K-Nearest Neighbor
  • K-Mean Clustering
  • Regression
  • Naive Bayes

Chapter 9. Applying ML algorithms        

  • Following the data
  • Fitting the data
  • Selecting the best algorithm

Chapter 10. What are Artificial Neural Networks?        

  • Using a neural network
  • Multilayer Perceptrons
  • Making decisions with neurons

Chapter 11. Neural Networks for Machine Learning        

  • Finding complex patterns
  • Feeding data into the network
  • Using hidden layers
  • Adding weights
  • Determining the activation level
  • Giving the neurons an activation bias
  • Using backpropagation for errors

Chapter 12. Building your team

  • Asking great questions
  • Working with a “knowledge explorer”
  • Turning questions into insights