There are many opportunities in applying machine learning, whether as an individual developer or in a business. But how do you get started? This talk provides an overview that separates fact from fiction and proposes processes to find opportunities for applying ML. This includes understanding where ML can have the biggest impact while avoiding common pitfalls. It emphasizes how improvements in processes can significantly outweigh algorithmic improvements. Some of the features examined are data collection and quality, definitions used (e.g, for labeling), metrics, objective functions, overfitting, and the cost of different types of errors, among others.