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