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Introduction to Artificial Intelligence (2023)

Introduction to Artificial Intelligence (2023)

1h 35mBeginner2023-03-15

Authors

Doug Rose

Doug Rose

Teaching Fortune 500s and professionals how to lead change

Course details

Computer scientists are just a small slice of people working in artificial intelligence. Most of the people working in AI are project managers, product managers, directors, and executives. People just like you. This course helps you grasp key concepts in artificial intelligence. You’ll see how AI can enhance your products, life, and career. AI has been around for over half a century. Even with its huge strides, the core ideas in machine learning and neural networks are still accessible.

This course is designed for project managers, product managers, directors, executives, and students starting a career in AI. Get a high-level overview of the top tools in the field. First, see what it means for a system to display “intelligence.” Then, learn algorithms and techniques involved in machine learning, artificial neural networks, and deep learning. Once simplified, AI looks less like magic and more like an exciting new set of technology tools.

Skills covered

Artificial Intelligence FoundationsArtificial Intelligence for BusinessArtificial Intelligence (AI)Business Analysis and StrategyOne-Off

Concepts

0. Introduction

  • 01 - Why you need to know about artificial intelligence

1. What Is Artificial Intelligence

  • 02 - Define general intelligence
  • 03 - The general problem-solver
  • 04 - Strong vs. weak AI

2. The Rise of Machine Learning

  • 05 - Machine learning
  • 06 - Artificial neural networks

3. Common AI Systems

  • 07 - Searching for patterns in data
  • 08 - Robotics
  • 09 - Natural language processing
  • 10 - The Internet of Things

4. Learn from Data

  • 11 - Labeled and unlabeled data
  • 12 - Massive datasets

5. Identify Patterns

  • 13 - Classify data
  • 14 - Cluster data
  • 15 - Reinforcement learning

6. Machine Learning Algorithms

  • 16 - Common algorithms
  • 17 - K-nearest neighbor
  • 18 - K-means clustering
  • 19 - Regression
  • 20 - Naive Bayes

7. Fit the Algorithm

  • 21 - Select the best algorithm
  • 22 - Follow the data
  • 23 - Overfitting and underfitting

8. Artificial Neural Networks

  • 24 - Build a neural network
  • 25 - Weighing the connections
  • 26 - The activation bias

9. Improve Accuracy

  • 27 - Learning from mistakes
  • 28 - Step through the network

10. Where to Go from Here

  • 29 - Using AI systems
  • 30 - Applying AI to solve problems

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