Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Introduction to AI, AI Programming and Machine Learning (AI / ML JumpStart) (TTML5503)
Introduction to Artificial Intelligence (AI) & Machine Learning (AI & ML JumpStart) is a three-day, foundation-level, hands-on course that explores the fast-changing field of artificial intelligence (AI). programming, logic, search, machine learning, and natural language understanding. Students will learn current AI / ML methods, tools, and techniques, their application to computational problems, and their contribution to understanding intelligence.
Course Overview
Overall Proficiency Level
2 - Intermediate
Course Catalog Number
TTML5503
Course Prerequisites
Students should have attended or have incoming skills equivalent to those in this course: Basic Understanding of Python as well as familiarity with Python Libraries (Pandas and NumPy, etc.). Attendees without Python background may view labs as follow along exercises or team with others to complete them. Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su Basic Math and Problem-Solving Skills Understanding of Basic Data Structures
Training Purpose
Skill Development
Specific Audience
Academia
Contractor
Federal Employee
Delivery Method
Classroom
Online, Instructor-Led
Course Location
4176 S Plaza Trail
Suite 207
Virginia Beach, VA 23452
Learning Objectives
This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Students will be led through a series of progressively advanced topics, where each topic consists of lecture, group discussion, comprehensive hands-on lab exercises, and lab review. Throughout the course students will learn about and explore popular machine learning algorithms, their applicability and limitations; practical application of these methods in a machine learning environment; and practical use cases and limitations of algorithms. Working in a hands-on learning environment led by our expert practitioner, attendees will explore: Getting Started with Python & Jupyter Statistics and Probability Refresher, and Python Practice Matplotlib and Advanced Probability Concepts Algorithm Overview Predictive Models Applied Machine Learning Recommender Systems Dealing with Data in the Real World Machine Learning on Big Data (with Apache Spark) Testing and Experimental Design GUIs and REST: Build a UI & REST API for your Models
Framework Connections
The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):
Work Roles
Feedback
If you would like to provide feedback on this course, please e-mail the NICCS team at NICCS@mail.cisa.dhs.gov. Please keep in mind that NICCS does not own this course or accept payment for course entry. If you have questions related to the details of this course, such as cost, prerequisites, how to register, etc., please contact the course training provider directly. You can find course training provider contact information by following the link that says “Visit course page for more information...” on this page.
Last Published Date: