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 [..]
Online Virtual Class
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