In this module, we look at how to improve the quality of our data and how to explore our data by performing exploratory data analysis. We look at the importance of tidy data in Machine Learning and show how it impacts data quality. For example, missing values can skew our results. You will also learn the importance of exploring your data. Once we have the data tidy, you will then perform exploratory data analysis on the dataset.

In this module, we will introduce some of the main types of machine learning so that you can accelerate your growth as an ML practitioner.

In this module, we will introduce training AutoML Models using Vertex AI.

In this module, we will introduce BigQuery ML and its capabilities.

In this module we will walk you through how to optimize your ML models.

Now it’s time to answer a rather weird question: when is the most accurate ML model not the right one to pick? As we hinted at in the last module on Optimization -- simply because a model has a loss metric of 0 for your training dataset does not mean it will perform well on new data in the real world. You will learn how to create repeatable training, evaluation, and test datasets and establish performance benchmarks.

This module is a summary of the Launching into Machine Learning course