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

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Noah Gift

1:00:46

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01 - Introduction

01 - Overview.mp4

01:25

02 - Course three key terminology.mp4

02:37

03 - Onboard to GCP.mp4

08:05

02 - 1. Exploring Data

01 - What is Google Colab.mp4

05:48

02 - Exploratory data analysis for life expectancy.mp4

06:53

03 - Data science setup with virtualenv and pip on Windows.mp4

03:28

04 - Graphing data for exploratory data analysis.mp4

03:46

03 - 2. Building Data Pipelines

01 - Labeling data.mp4

01:24

02 - Mechanical Turk labeling.mp4

01:35

03 - Cleaning up data.mp4

01:28

04 - Scaling data.mp4

01:30

05 - BigQuery data pipelines with Colab.mp4

05:32

04 - 3. Creating Input Features

01 - Feature engineering concepts.mp4

01:34

02 - Extracting features from public datasets.mp4

01:44

03 - Exploratory data analysis with Google BigQuery.mp4

12:36

05 - Conclusion

01 - Next steps.mp4

01:21

Description

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.

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Machine Learning

Data Science

Big Data

Noah Gift

Instructor's Courses

Author of Practical MLOps, Enterprise MLOps, Developing on AWS with C#, Pragmatic AI, and Python for DevOps. Certified on Multiple MLOps certifications including Google-Professional Machine Learning Engineer and AWS Certified Machine Learning - Specialty. Adjunct Professor at Duke MIDS & Northwestern Graduate Data Science & AI. Held business roles including CTO, general manager, consulting CTO, and cloud architect. Consults with start-ups and other companies on machine learning and cloud architecture. AWS ML Hero, Python Software Foundation Fellow, AWS Subject Matter on Machine Learning, AWS Certified Solutions Architect, AWS Certified Machine Learning Specialist, AWS Certified Big Data Specialist, Google Certified Professional Architect, and AWS Academy Accredited Instructor. Published books, videos, and courses on cloud machine learning, DevOps, Python, Data Science, Big Data and AI. ★ Specialties ★ ° Cloud-native Machine Learning and AI ° Directly teaching cutting edge skills to students that lead to jobs ° Creating world-class content in all forms ° Building Companies ° Shipping new Products ° Leading and growing engineering teams ° Production Machine Learning, Deep Learning, Big Data, and AI ° Serverless Data Engineering ° Advising Early Stage Startups/Consulting CTO services ° Distributed Systems and Scalability

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.