Google Cloud Professional Data Engineer: Get Certified 2020 - Freewebcart
Google Cloud Professional Data Engineer: Get Certified 2020 Build scalable, reliable data pipelines, databases, and machine learning applications. How to pass
Course Provider: Organization
Course Provider Name: Dan Sullivan
Course Provider URL: https://www.udemy.com/user/dan-sullivan-3/
Google Cloud Professional Data Engineer: Get Certified 2020 Build scalable, reliable data pipelines, databases, and machine learning applications.
What you’ll learn
How to pass the Google Cloud Professional Data Engineer Exam
Build scalable, reliable data pipelines
Choose appropriate storage systems, including relational, NoSQL and analytical databases
Apply multiple types of machine learning techniques to different use cases
Deploy machine learning models in production
Monitor data pipelines and machine learning models
Design scalable, resilient distributed data intensive applications
Migrate data warehouse from on-premises to Google Cloud
Evaluate and improve the quality of machine learning models
Grasp fundamental concepts in machine learning, such as backpropagation, feature engineering, overfitting and underfitting.
Google Cloud Professional Data Engineer: Get Certified 2020 Description
The need for data engineers is constantly growing and certified data engineers are some of the top paid certified professionals. Data engineers have a wide range of skills including the ability to design systems to ingest large volumes of data, store data cost-effectively, and efficiently process and analyze data with tools ranging from reporting and visualization to machine learning.
Earning a Google Cloud Professional Data Engineer certification demonstrates you have the knowledge and skills to build, tune, and monitor high performance data engineering systems.
This course is designed and developed by the author of the official Google Cloud Professional Data Engineer exam guide and a data architect with over 20 years of experience in databases, data architecture, and machine learning.
This course combines lectures with quizzes and hands-on practical sessions to ensure you understand how to ingest data, create a data processing pipelines in Cloud Dataflow, deploy relational databases, design highly performant Bigtable, BigQuery, and Cloud Spanner databases, query Firestore databases, and create a Spark and Hadoop cluster using Cloud Dataproc.
The final portion of the course is dedicated to the most challenging part of the exam: machine learning. If you are not familiar with concepts like backpropagation, stochastic gradient descent, overfitting, underfitting, and feature engineering then you are not ready to take the exam. Fortunately, this course is designed for you.
In this course we start from the beginning with machine learning, introducing basic concepts, like the difference between supervised and unsupervised learning. We’ll build on the basics to understand how to design, train, and evaluate machine learning models.
In the process, we’ll explain essential concepts you will need to understand to pass the Professional Data Engineer exam. We’ll also review Google Cloud machine learning services and infrastructure, such as BigQuery ML and tensor processing units.