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You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis,...

You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the da...

When valuing a company, a key part of the valuation is not just the cash flows that'll be generated for the next few years, but ultimately, what the company is going to be worth far down the road, what we call the terminal value. I'm in the 04_03_Begin Excel f...

In addition to financial models built around the three different major financial statements, we'll often to be asked to build financial models around stock price data and the associated financials. Now, two types of models that we might be asked to build are t...

You are developing ML models with AI Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You w...

Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with...

You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations AI to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices? ...

You are designing an architecture with a serverless ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perfo...

Welcome to Launching into Machine Learning. In this course, you'll get foundational ML knowledge so that you understand the terminology we use throughout the courses. In this course, you'll learn how to improve data quality, perform exploratory data analysis...

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...

In the course, you will learn that there are two phases in machine learning, a training phase and an inference phase. You will see that an ML problem can be thought of as being all about data. In any ML project, after you define the best use case and establ...

You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation data. You want the model to be resilient to overfitting. Which strategy should you use when retraining the mode...

You built and manage a production system that is responsible for predicting sales numbers. Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn't changed; however...

You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best...

You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your model's features include region, location, historical demand, and seasonal popularity. You want the algorithm ...

This lab focuses on improving data quality. Recall that machine learning models can only consume numeric data and that numeric data should be ones or zeroes. Data is said to be messy or untidy if it is missing attribute values, contains noise or outliers, ha...

  Materials https://github.com/GoogleCloudPlatform/training-data-analyst

Overview Machine learning models can only consume numeric data, and that numeric data should be 1s or 0s. Data is said to be messy or untidy if it is missing attribute values, contains noise or outliers, has duplicates, wrong data, or upper/lower case column ...

You are building a real-time prediction engine that streams files which may contain Personally Identifiable Information (PII) to Google Cloud. You want to use theCloud Data Loss Prevention (DLP) API to scan the files. How should you ensure that the PII is not ...

You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 20 days so that marketing can be adjusted a...