ML Course from AWS

Building ML applications is an iterative process that involves a sequence of steps. To build an ML application, follow these general steps:

Artificial intelligence ingests data, such as human-level knowledge, and imitates natural intelligence. Machine learning is a subset of AI, where data and algorithms continuously improve the training model to help achieve higher-quality output predictions. Deep learning is a subset of machine learning. ML is a technique for realizing AI.

Machine learning involves teaching a computer to recognize patterns by example, rather than programming it with specific rules. These patterns can be found in the data.

ML can provide predictive solutions (regression and classification), prioritization (rankings and scores), and behavior patterns (recommendations and clustering).

Simple and complex ML models differ when balancing a model's accuracy (number of correctly predicted data points) and a model's explainability (how much of the ML system can be explained in "human terms"). The output of a simple ML model may be explainable and produce faster results, but the results may be inaccurate. The output of a complex ML model may be accurate, but the results may be difficult to communicate.

Unexplainability represents how much of the reasoning behind an ML model's decision cannot be effectively described in human terms. There are potentially legal, professional, ethical, and regulatory conditions where the tolerance for unexplainability may vary from case to case.

When is it not okay?

• When you need to be able to explain to your customer why a loan was declined
• When you need to be able to explain why a transaction was deemed fraudulent

When is it okay?

• When risks of misclassification are low, such as object recognition for catalog search or predicting the probability of completing NFL play
• When humans make the final decisions

The machine learning lifecycle consists of nine stages.