DeepLearning.AI - Practical Data Science on the AWS Cloud Specialization

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Practical Data Science on the AWS Cloud Specialization
at
Coursera
Overview

Develop and scale your data science projects into the cloud using Amazon SageMaker

Duration	3 months
Mode of learning	Online
Difficulty level	Advanced
Credential	Certificate

Practical Data Science on the AWS Cloud Specialization
at
Coursera
Highlights

Earn a certificate of completion from Deep Learning

Practical Data Science on the AWS Cloud Specialization
at
Coursera
Course details

Skills you will learn

Python Data Science

What are the course deliverables?

Ingest, register, and explore datasets
Detect statistical bias in a dataset
Automatically train and select models with AutoML
Create machine learning features from raw data
Save and manage features in a feature store
Train and evaluate models using built-in algorithms and custom BERT models
Debug, profile, and compare models to improve performance

More about this course

The Practical Data Science Specialization brings together these disciplines using purpose-built ML tools in the AWS cloud
It helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker
This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages who want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud
Each of the 10 weeks features a comprehensive lab developed specifically for this Specialization that provides hands-on experience with state-of-the-art algorithms for natural language processing (NLP) and natural language understanding (NLU), including BERT and FastText using Amazon SageMaker

Practical Data Science on the AWS Cloud Specialization
at
Coursera
Curriculum

Analyze Datasets and Train ML Models using AutoML

Build, Train, and Deploy ML Pipelines using BERT

Optimize ML Models and Deploy Human-in-the-Loop Pipelines

Practical Data Science on the AWS Cloud Specialization
at
Coursera
Faculty details

Antje Barth

Antje Barth is a Sr. Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS). She is co-author of the O'Reilly book - Data Science on AWS. Antje frequently speaks at AI / ML conferences, events, and meetups around the world.

Shelbee Eigenbrode

Shelbee Eigenbrode is a Principal AI and Machine Learning Specialist Solutions Architect at Amazon Web Services (AWS). She holds 6 AWS certifications and has been in technology for 23 years spanning multiple industries, technologies, and roles.

Sireesha Muppala

Sireesha Muppala is an Enterprise Principal SA, AI/ML at Amazon Web Services (AWS) who guides customers on architecting and implementing machine learning solutions at scale.

Chris Fregly

Chris Fregly is a Principal Engineer for AI and Machine Learning at Amazon Web Services (AWS) based in San Francisco, California. He is co-author of the O'Reilly Book, "Data Science on AWS."