Get a simplified overview of the top tools in artificial intelligence.
Overview
Syllabus
Introduction
- Why you need to know about artificial intelligence
- Define general intelligence
- The general problem-solver
- Strong vs. weak AI
- Machine learning
- Artificial neural networks
- Searching for patterns in data
- Robotics
- Natural language processing
- The Internet of Things
- Labeled and unlabeled data
- Massive datasets
- Classify data
- Cluster data
- Reinforcement learning
- Common algorithms
- K-nearest neighbor
- K-means clustering
- Regression
- Naive Bayes
- Select the best algorithm
- Follow the data
- Overfitting and underfitting
- Build a neural network
- Weighing the connections
- The activation bias
- Learning from mistakes
- Step through the network
- Using AI systems
- Applying AI to solve problems
Taught by
Doug Rose