Video: .MP4, 1280x720 30 fps | Audio: AAC, 44.1 kHz, 2ch | Duration: 7h

Genre: eLearning | Language: English | Size: 9.7 GB

This course covers the essentials of Machine Learning on AWS and prepares a candidate to sit for the AWS Machine Learning-Specialty (ML-S) Certification exam. Four main categories are covered: Data Engineering, EDA (Exploratory Data Analysis), Modeling, and Operations. Description This 7+ hour Complete Video Course is fully geared toward the AWS Machine Learning-Specialty (ML-S) Certification exam. The course offers a modular lesson and sublesson approach, with a mix of screencasting and headhsot treatment.

Data Engineering instruction covers the ingestion, cleaning, and maintenance of data on AWS.

Exploratory Data Analysis covers topics including data visualization, descriptive statistics, and dimension reduction and includes information on relevant AWS services.

Machine Learning Modeling covers topics including feature engineering, performance metrics, overfitting, and algorithm selection.

Operations covers deploying models, A/B testing, using AI services versus training your own model, and proper cost utilization.

The supporting code for this LiveLesson is located at.

About the Instructor

Noah Gift is a lecturer and consultant at both the UC Davis Graduate School of Management MSBA program and the Graduate Data Science program, MSDS, at Northwestern. He teaches and designs graduate machine learning, AI, data science courses, and consulting on machine learning and cloud architecture for students and faculty. These responsibilities include leading a multi-cloud certification initiative for students. Noah is a Python Software Foundation Fellow, AWS Subject Matter Expert (SME) on Machine Learning, AWS Certified Solutions Architect, AWS Academy accredited instructor, Google Certified Professional Cloud Architect, and Microsoft MTA on Python. Noah has published close to 100 technical publications including two books on subjects ranging from cloud machine learning to DevOps.

Noah received an MBA from UC Davis, a M.S. in Computer Information Systems from Cal State Los Angeles, and a B.S. in Nutritional Science from Cal Poly San Luis Obispo. Currently he consults for startups and other companies on machine learning, cloud architecture, and CTO-level consulting as the founder of Pragmatic AI Labs. His most recent publications are Pragmatic AI: An introduction to Cloud-Based Machine Learning(Pearson, 2018) and Essential Machine Learning and AI with Python and Jupyter Notebook LiveLessons (Video Training).

Skill Level

Intermediate

What You Will Learn

How to perform data engineering tasks on AWS

How to use Exploratory Data Analysis (EDA) to solve machine learning problems on AWS

How to perform machine learning modeling tasks on the AWS platform

How to operationalize machine learning models and deploy them to production on the AWS platform

How to think about the AWS Machine Learning-Specialty (ML-S) Certification exam to optimize for the best outcome

Who Should Take This Course

DevOps engineers who want to understand how to operationalize ML workloads

Software engineers who want to ensure they have a mastery of machine learning terminology and practice on AWS

Machine learning engineers who want to solidify their knowledge about AWS machine learning practices

Product managers who need to understand the AWS machine learning lifecycle

Data scientists who run machine learning workloads on AWS

Course Requirements

One to two years of experience with AWS and six months using ML tools. Ideally, candidates will have already passed the AWS Cloud Practitioner certification.