Machine Learning is a broad concept that involves AI, Statistics and Algorithms.
- Like AI, Machine Learning is about processing and understanding data to react inteligently upon it.
- Like Statistics, it is about applying complex formulas to make sense, compare, evaluate and summarize data.
- Like Algorithms, it operates on data input and provides some output.
In conclusion, Machine Learning is an evolution of all these areas.
In traditional programming, we write decisions into the code. In machine learning, we create an agent that is trained and can figure patterns by itself.
For example, in traditional programming, we would write code that would use a static set of algorithms to identify certain features in a image and figure out if it was a determinate person or not. In machine learning, we code an agent that is trained with a bunch of pictures and figure by itself how to identify a certain person.
- Machine Learning and AI entire landscape
- Building model and validation
- Nuts and bolts of machine learning: different tools for different kinds of problems and when to use them
- Personnal Project using all the knowledge gathered during the program.