Introduction
What is Applied Machine Learning
This course focused more on how to properly apply and interpret the results from machine learning algorithms at a high level and less on the technical details of exactly how these algorithms work internally.
Key Concepts in Machine Learning
Machine learning tasks can be categorized into two main types.
1. Supervised machine learning: Learn to predict target values from labelled data.
- Classification: target values are discrete classes
- Regression: target values are continuous values
Here is an example about classification problem about image recognition. Our goal is to learn some function that maps data item in X to a label in Y. 
Supervised learning needs to have this training set with labeled objects in order to make its predictions.
But a key point is where does this initial set of label items come from? The answer is that the training labels are typically provided by human judges. That could be difficult, depending on how much labeled data is needed.
2. Unsupervised machine learning: Find useful structure or knowledge in unlabeled data
- Find groups of similar instances in the data (clustering)
- Finding unusual patterns (outlier detection)
Once we can discover this structure in the form of clusters, groups or other interesting subsets. The structure can be used for tasks like producing a useful summary of the input data maybe visualizing the structure.
Python Tools for Machine Learning