Google Cloud Professional Machine Learning Engineer Cert Prep: 3 Designing Data Preparation and Processing Systems

سرفصل های دوره

Earning a Google Professional Machine Learning Engineer certification demonstrates your ability to design, build, and productionize machine learning models to solve business challenges using Google Cloud technologies, and knowledge of proven ML models and techniques.

In this third course in the certification prep series, instructor Noah Gift covers designing data preparation and processing systems. He covers topics you need to know relating to exploratory data analysis (EDA), then shows how to build data pipelines and create input features.

01 - Introduction

01 - Overview

02 - Course three key terminology

03 - Onboard to GCP

02 - 1. Exploring Data

01 - What is Google Colab

02 - Exploratory data analysis for life expectancy

03 - Data science setup with virtualenv and pip on Windows

04 - Graphing data for exploratory data analysis

03 - 2. Building Data Pipelines

01 - Labeling data

02 - Mechanical Turk labeling

03 - Cleaning up data

04 - Scaling data

05 - BigQuery data pipelines with Colab

04 - 3. Creating Input Features

01 - Feature engineering concepts

02 - Extracting features from public datasets

03 - Exploratory data analysis with Google BigQuery

05 - Conclusion

01 - Next steps

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تولید کننده: LinkedIn Learning (Lynda)