Beyond Code Debugging
Artificial intelligence (AI), like human intelligence, is a capability and tool that can be used for decision-making and task accomplishment. As humans, we use our intelligence in making our daily decisions and thinking about the challenges and problems we deal with. We use our brains and central nervous systems to receive information from our surroundings and process them for decision-making and reactions.
Machine learning models are the AI techniques that are used nowadays to tackle problems across healthcare and finance. Machine learning models have been used in robotic systems in manufacturing facilities to package products or identify products that might have been damaged. They have been used in our smartphones to identify our faces for security purposes, by e-commerce companies to suggest the most suited products or movies to us, and even for improving healthcare and drug development to bring new more effective drugs onto the market for severe diseases.
In this chapter, we will provide a quick review of different types of machine learning modeling. You will learn about different techniques and challenges in debugging your machine learning code. We will also discuss why debugging machine learning modeling goes far beyond just code debugging.
We will cover the following topics in this chapter:
- Machine learning at a glance
- Types of machine learning modeling
- Debugging in software development
- Flaws in data used for modeling
- Model and prediction-centric debugging
This chapter is an introduction to this book to prepare you for more advanced concepts that will be presented later. This will help you improve your models and move toward becoming an expert in the machine learning era.
