This track is CLOSED, but you can register to join the “wait list.” Attendees on the “wait list” will placed for now in their alternate, second-choice track and then notified if and when space opens up in their “wait list” track.

Description
Artificial Intelligence is used in a variety of fields in ways most do not even realize today. Most people think of coding in Python to do in-depth statistical analysis with machine learning when it comes to AI. However, there is much that can be done today using existing tools with no/low code and then allow you to make deeper changes as your knowledge in Machine Learning grows.

No prior programming knowledge is required. While both Java and Python are used to introduce concepts, neither will be expected to be known or used in depth by participants.

Textbook
None. Materials will be provided during the class and some will be available prior to the start of the course.

Prerequisites
None. Having an understanding of Linux, data structures, data systems including SQL vs. NoSQL are helpful, but not required.

At-Home Computer Requirements

• Good internet connection
• Student email access to Oracle Academy, AWS Educate and AWS Academy. Obtaining instructor account access can take weeks or months to obtain, so a non-faculty email is required to be sent to the instructor trainer for this course and verified by Friday, June 25.
• Dial-up and log-on details for the online sharing platform will be provided

All Summer Working Connections attendees will need a strong and dependable internet connection. A web camera is preferred to help with class interactions.

Instructor

As an undergraduate at Texas A & M, Matthew Cloud created a company, Cloud Technologies, building systems ranging from Positron Emission Tomography calibration to logistics systems for the 2nd layer of the US network in the late 90’s, to turning in 2008 towards understanding the network of the human brain as a biomedical engineer (MS) to help those with Stroke, TBI, pain, PTSD or ADHD recover. For four years prior to his current role, he was Director of a DOL grant defining the IT Pathways over the School of IT at the State level of Ivy Tech CC of Indiana with over 22,000 IT students and 150 faculty. Currently, as the Director of Cybersecurity Grants and School of IT Department Chair for the Ivy Tech Lake County campus he also serves as State Vice-Chair for Computer Science. He is an advisor to the Indiana Governors Cybersecurity Council, CISCO Academy and NetAcad 4 Diverse Abilities, and think tanks for Federal Reserve Boards and the National Skills Coalition building towards a National Training Center for AI.

Track Objectives
At the completion of this track, the participants will be able to…

• Demonstrate the major concepts of Artificial Intelligence (AI) and Machine Learning (ML)
• Utilize the latest AI tools and industry based academy resources to enhance work performance.
• Determine which AI and ML tools to use for different scenarios
• Understand how AI and ML modules can fit into various college curricula at an introductory level and the AI technician job market trends.

Agenda

Day 0
All participants before the first day should

• 1. Read the materials provided by the instructor trainer prior to the start of class.
• 2. Verify student access to Oracle Academy and Amazon Educate
• 3. Have available a system with the latest versions of the following software installed.
  • a. Python IDLE 3.6 or higher. Be sure to set the PATH when installing.
  • b. Flowgorithm
  • c. Raptor

Each day will have an discussion with practical application based on the concepts presented throughout the day. Each day will also have a quiz leading to preparation for the AWS Machine Learning Badge.

Day 1

• 1. Introductions – Instructor and Participants
• 2. Pretest-Quiz
• 3. What is Data Science, AI/ML, and Low-code/No-code?
• 4. Logic Tool - Flowgorithm
• 5. Oracle Academy resources
• 6. AWS Educate Resources
• 7. AWS Academy Resources
• 8. Classification
• 9. Code.org AI Exercise

Day 2

• 1. Quiz over Day 1 materials
• 2. Information Entropy
• 3. Information Gain
• 4. Binary Trees
• 5. Logic Tool - Raptor
• 6. Oracle AI Digital Assistant

Day 3

• 1. Quiz over Day 2 materials (Entropy/Gain)
• 2. Iterative Decision Making (ID3)
• 3. Go/No Go exercises
• 4. Customizing the Digital Assistant

Day 4

• 1. Quiz over Day 3 materials (ID3)
• 2. AWS Natural Language Processing
  • a. Amazon Comprehend
  • b. Amazon Polly
  • c. Amazon Translate
• 3. AWS Amazon Lex Chatbot
• 4. Computer Vision
• 5. Deeper into Machine Learning models
  • a. Supervised vs. Unsupervised
  • b. Credit models
  • c. Sagemaker (if working. This tools works great when it works, but has had issues with proper access being allowed for academic use)

Day 5

• 1. Quiz over Day 4 materials
• 2. Implementing ML pipelines
  • a. Extract, Transform and Load
  • b. Secure Data
  • c. Describe Data
  • d. Explore and find correlations
  • e. Feature Engineering
    • i. Cleaning
    • ii. Outliers
    • iii. Encoding
  • f. Training Model
  • g. Deploying Model
  • h. Evaluate Accuracy of Model
  • i. Model Tuning
• 3. Forecasting

Advanced Concepts as time allows

• 1) AI/ML Local vs. cloud based VM’s
• 2) Python based ML
  • a. Oracle
  • b. AWS
  • c. Custom

NOTE: Labs may change based upon changes in the industry, and constantly updating models and languages.

Please note also: the schedule is subject to change or be modified based on the needs of the track participants.