Summary

We are now heading to the end of this chapter, where we have covered several important topics about the foundations of ML. We started the chapter with a theoretical discussion about AI, ML, and DL and how this entire field has grown over the past few years due to the advent of big data platforms and cloud providers.

We then moved on to the differences between supervised, unsupervised, and reinforcement learning, highlighting some use cases related to each of them. This is likely to be a topic in the AWS Machine Learning Specialty exam.

We discussed that an ML model is built in many different stages and the algorithm itself is just one part of the modeling process. We also covered the expected behaviors of a good model.

We did a deep dive into data splitting, where we talked about different approaches to train and validate models, and we covered the mythic battle between variance and bias. We completed the chapter by talking about ML frameworks and services.

Coming up next, you will learn about AWS application services for ML, such as Amazon Polly, Amazon Rekognition, Amazon Transcribe, and many other AI-related AWS services. But first, let's look into some sample questions to give you an idea of what you could expect in the exam.