Testimonials

NIST Cybersecurity Framework Bootcamp — The in-depth look into not only what the standards are, but how to apply them, was extremely helpful. I very much appreciate the practical examples that were given.

MSPL-900T00: Microsoft Power Platform Fundamentals — Instructor did an awesome job tying the material we were learning to real world scenarios and giving examples of how he has used in the real world.

Microsoft Excel 2019: Part 1 — The shortcuts that I learned; they will save me so much time in the future. I plan to apply this content to future spreadsheets I will create and/or modify.

CompTIA CySA+ Cybersecurity Analyst — Everything I learned had real-life examples of what I experience in my work field on a day-to-day basis.

AZ-040T00: Automating Administration with Windows PowerShell — Instructor took personal interest in what we were there for and kept an upbeat tone for each lesson. He was able to expound upon any question we asked.

Microsoft Office 365 Online (with Teams for the Desktop) — I knew nothing about 365. That has changed. Thank you!

Implementing and Configuring Cisco Identity Services Engine (SISE) — The class was great at teaching all the different parts of Cisco ISE and the labs were very helpful with setting up a new server and configuring.

NIST Cybersecurity Framework Foundation — Thanks to the Cybersecurity Foundation course, I feel confident in my ability to help assess the state of our current profile and to help implement changes to make our website safer.

AWS-MLDWTS - The Machine Learning Pipeline on AWS

This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.

Instructor did a great job, from experience this subject can be a bit dry to teach but he was able to keep it very engaging and made it much easier to focus. Student

Excellent presentation skills, subject matter knowledge, and command of the environment. Student

Instructor was outstanding. Knowledgeable, presented well, and class timing was perfect. Student

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Prerequisites

Basic knowledge of Python
Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
Basic understanding of working in a Jupyter notebook environment

Detailed Class Syllabus

Outline

Module 1: Introduction to Machine Learning and the ML Pipeline
Module 2: Introduction to Amazon SageMaker
Module 3: Problem Formulation
Module 4: Preprocessing
Module 5: Model Training
Module 6: Model Evaluation
Module 7: Feature Engineering and Model Tuning
Module 8: Deployment

AWS-MLDWTS - The Machine Learning Pipeline on AWS

Student Testimonials

Prerequisites

Detailed Class Syllabus

Outline