Machine Learning Academy

Field Guide to Machine Learning, Lesson 1: Problem Definition

The Facebook Field Guide to Machine Learning is a six-part video series developed by the Facebook ads machine learning team. The series shares best real-world practices and provides practical tips about how to apply machine-learning capabilities to real-world problems.

If you’re interested in using machine learning to enhance your product in the real world, it’s important to understand how the entire development process works. It’s not only what happens during the training of your models, but everything that comes before and after, and how each step can either set you up for success or doom you to fail.

The Facebook ads machine learning team has developed a series of videos to help engineers and new researchers learn to apply their machine learning skills to real-world problems. The Facebook Field Guide to Machine Learning series breaks down the machine learning process into six steps:

1. Problem definition
2. Data
3. Evaluation
4. Features
5. Model
6. Experimentation

This video series covers each of these steps, explaining how the decisions you make along the way can help you successfully apply machine learning to your product or use case. Each lesson highlights examples and stories of non-obvious things that can be important in an applied setting.

Lesson 1: Problem definition. In this lesson we share best practices about defining the problem. How the right set up is often more important than the choice of algorithm, and why a few hours spent at this stage in the process can save many weeks work further downstream, preventing you from solving the wrong problem.

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