Who it is designed for
6–8 participants in the Tek2KidX learning lane. Final placement considers readiness, prerequisites, safety, support needs, and confirmed cohort design.
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Machine Learning & Computer Vision
Machine Learning & Computer Vision is an instructor-led artificial intelligence experience for 6–8. Learners build essential knowledge, apply it through coached hands-on work, test and improve their decisions, and communicate what they created and learned.
Artificial Intelligence
Machine Learning & Computer Vision
01LearnBuild essential knowledge
02ApplyPractice through meaningful work
03ShowDemonstrate and reflect
PUBLISHED TUITION$2,149
per 45-hour cohort. Confirmed session dates and payment terms appear before enrollment.
PROGRAM FORMAT10-week lab
Monday–Thursday · 4:00–7:00 PM. Weekend sessions supplement the core schedule.
LEARNER GROUP6–8
Placement considers age, readiness, prerequisites, safety, and the selected delivery setting.
DELIVERY & EVIDENCEHybrid · Train and evaluate a small vision model
In-person learning is paired with controlled online support; progress depends on demonstrated work.
COURSE DESCRIPTION
What this learning experience offers.
Machine Learning & Computer Vision is an instructor-led artificial intelligence experience for 6–8. Learners build essential knowledge, apply it through coached hands-on work, test and improve their decisions, and communicate what they created and learned.
The learning sequence is designed to help participants move from understanding to application and then to visible evidence. Final activities, tools, staffing, prerequisites, and session details are confirmed before enrollment.
THE COMPLETE COURSE VIEW
Who, what, where, when, why, how—and ways to participate.
Clear answers help families understand the experience before expressing interest or enrolling.
What learners will experience
An active Artificial Intelligence program delivered as a 10-week lab, focused on this outcome: train and evaluate a small vision model.
Where learning happens
Hybrid delivery through a confirmed KaizenX Academy location or approved partner site, supported by controlled online resources when appropriate. The exact location appears with each session.
When sessions are offered
10-week lab. Standard service windows are Monday–Thursday, 4:00–7:00 PM, and weekends, 10:00 AM–2:00 PM. A confirmed listing provides exact dates and attendance expectations.
Why the course matters
It connects knowledge with purposeful application, helping learners build confidence, problem-solving ability, responsible technology habits, and clearer awareness of future learning pathways.
How learners progress
Instructor-led explanation, demonstration, guided practice, team or individual application, testing, feedback, improvement, documentation, presentation, and reflection.
Ways to participate
Depending on the confirmed offering: family-paid tuition, institutional or sponsored cohorts, after-school delivery, weekend labs, camps, clubs, tutoring, pathway study, or competition-aligned experiences.
WHAT TUITION INCLUDES
Clear pricing before enrollment.
Advanced lab instruction, shared equipment, standard build materials, technical feedback, and portfolio evidence.
Any optional travel, lodging, third-party registration, premium consumables, examination charges, or custom-build costs not included in tuition will be identified before a parent or guardian enrolls.
Open complete pricing and payment informationLEARNING OBJECTIVES
What learners will learn and develop
- How machine learning systems use data
- Classification and model evaluation
- Bias, privacy, and responsible use
- Human oversight and communication
HANDS-ON EXPERIENCE
What learners will create and do
- 01Ethical classifier prototype
A coached, age-appropriate experience with documentation, testing, feedback, and improvement.
- 02Model test report
A coached, age-appropriate experience with documentation, testing, feedback, and improvement.
- 03AI impact presentation
A coached, age-appropriate experience with documentation, testing, feedback, and improvement.
OUTCOME & ACHIEVEMENT
What learners can work toward achieving.
The intended course outcome is to train and evaluate a small vision model. Achievement is demonstrated through completed work and evidence—not attendance alone.
Explain essential concepts and make responsible decisions.
Use appropriate tools and methods in guided work.
Produce a prototype, analysis, performance, portfolio item, or learning artifact.
Present decisions, results, improvements, and reflection.
Tools and learning environment
Python or visual AI toolsCurated datasetsModel cardsPresentation tools
Specific equipment and platforms may vary by session. Tools are used with instructor approval, safe-use procedures, and age-appropriate supervision.
Safety, prerequisites, and boundaries
- Parent- or guardian-managed enrollment and permissions for minors
- Prerequisites provided with session information
- Moderated communication and controlled portfolio sharing
- Supplemental education; no degree or occupational license is awarded