AI fundamentals mastered
ML principles understood
Professional credentials building
Computer Vision Specialist
Detect tumors in X-rays and scans
Spot defects on assembly lines
Face recognition and monitoring
Detect crop disease from drones
Train models to recognize diseases
Spot defects on assembly lines
Understand customer behavior
Recognize crop health from drones
Face recognition and surveillance
Autonomous systems, robotics, analytics
YOU SEE: A complete image instantly
MACHINE SEES: Thousands of pixel values (0-255)
TOGETHER: Human creativity + Machine consistency = Powerful
Hospital: 8 hours → 2 hours image review. That's 6 hours saved daily.
Factory: One recall prevented = millions saved. AI checks 100 items/min.
Retailer: Customer behavior insights = smarter layouts = more sales.
Real ML model trained on your dataset
Classifies images with documented accuracy
Employers recognize this immediately
Worth $60K-$80K starting salary
Choose use case, load dataset (15 min)
Tell platform what to build (20 min)
Model learns patterns (40 min)
Check accuracy, document (25 min)
Medical: Classify X-rays (pneumonia vs. normal)
Manufacturing: Detect defects (okay vs. defective)
Documents: Classify type (invoice, receipt, contract)
Agriculture: Plant health (healthy, diseased, stressed)
Retail: Product condition (new vs. used)
Security: Detect objects (person, vehicle, animal)
Wildlife: Classify animals or land use
Place training images in folders
Training, validation, test split
Images must be .jpg or .png
You have 3+ classes defined
Name: FirstName_LastName_ImageClassification
Specify classes: "Normal, Pneumonia, Other"
Training budget: 0.5-1.0 hours
Enable augmentation to prevent overfitting
Click START TRAINING
Step 1: Analyze 1,000+ training images
Step 2: Find patterns that separate classes
Step 3: Adjust weights until highly accurate
Step 4: Validate learning on test data
Activity 1: What mistakes might your model make? Why?
Activity 2: What features matter to your model?
Activity 3: How do you ensure fairness across demographics?
These aren't homework. They're real ML challenges professionals face.
% classified correctly (70%+ is good)
When it predicted X, was it right?
How many real cases did it catch?
What did it get confused about?
Problem statement: What are you solving?
Dataset details: Images, classes, split
Model performance: Accuracy, precision, recall
What worked well: Specific successes
Business application: How would companies use this?
Accuracy metrics and confusion matrix
All documentation filled in completely
Say your pitch out loud to pod
Get peer feedback and improve
3-4 of you will share what you built
2 minutes each
Everyone listens and gives feedback