Course #:TP2713

Discovering and Delivering Artificial Intelligence Products Training

Many data-centric organizations rely on manufacturing processes to build their products.  They’ll use a deterministic process where you “plan the work and then work the plan.” This mindset works great for products with linear development. But artificial intelligence products seldom follow this direct path. Teams aren’t manufacturing AI products as much as they’re discovering them. To effectively create new AI products your team needs to understand the technology and be comfortable running small experiments.  This course will go over artificial intelligence concepts and then show you specific practices such as machine learning and neural networks.  Then you’ll see roles and processes that encourage your team to use a less deterministic approach to discovering great AI products.

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 of 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
We regularly offer classes in these and other cities. Atlanta, Austin, Baltimore, Calgary, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Jacksonville, Miami, Montreal, New York City, Orlando, Ottawa, Philadelphia, Phoenix, Pittsburgh, Seattle, Toronto, Vancouver, Washington DC.