- 0 - Introduction
- 1. Why you need to know about artificial intelligence
- 1 - What Is Artificial Intelligence
- 1. Define general intelligence
- 2. The general problem-solver
- 3. Strong vs. weak AI
- 2 - The Rise of Machine Learning
- 1. Machine learning
- 2. Artificial neural networks
- 3 - Common AI Systems
- 1. Searching for patterns in data
- 2. Robotics
- 3. Natural language processing
- 4. The Internet of Things
- 4 - Learn from Data
- 1. Labeled and unlabeled data
- 2. Massive datasets
- 5 - Identify Patterns
- 1. Classify data
- 2. Cluster data
- 3. Reinforcement learning
- 6 - Machine Learning Algorithms
- 1. Common algorithms
- 2. K-nearest neighbor
- 3. K-means clustering
- 4. Regression
- 5. Naive Bayes
- 7 - Fit the Algorithm
- 1. Select the best algorithm
- 2. Follow the data
- 3. Overfitting and underfitting
- 8 - Artificial Neural Networks
- 1. Build a neural network
- 2. Weighing the connections
- 3. The activation bias
- 9 - Improve Accuracy
- 1. Learning from mistakes
- 2. Step through the network
- 10 - Where to Go from Here
- 1. Using AI systems
- 2. Applying AI to solve problems