A new approach to Artificial Intelligence with an emphasis on statistical analysis, the ability for the computer to write its own set of rules, rather than humans writing all of the conditional logic.
Its purpose
Using the power of computers to complement and supplement human intelligence.
Example: programs can scan and process huge databases detecting patterns that are beyond the scope of human perception.
Examples
Face/voice detection on your phone
Detecting eye deceases
Self-driving cars
Steps involved
Defining a problem
Preparing data
Evaluating algorithms
Improving results
Presenting results
Frameworks
Scikit-learn
TensorFlow
Machine learning on AWS
IBM Watson
Concepts of Learning
Categories of Learning
Supervised Learning
Unsupervised Learning
Semi-supervised Learning
Categories of algorithms
Supervised Learning Algorithm
Unsupervised Learning Algorithm
Semi-supervised Learning Algorithm
Reinforcement Learning Algorithm
Supervised Learning
Can be classified into 2 types:
Regression
Classification
When a machine intelligence predicts a category or a quantity using models of classification and regression respectively.
