1. Feature Inputs & Weights
Adjust visitor behavior & model hyperparameters
Candidate Video To Score
My First Step into AI and Machine Learning
Item Tags:
IoT
Python
Beginner
14 mins
1 min (Skimmer)
20 mins
40 mins (Deep learner)
8
2
Click to add/remove
0.60
Score required to classify as "Recommended" vs "Not Recommended"
2. Model Decision & Confidence
Real-time prediction computed from weighted inputs
Model Active
0.92
Score
✓
Recommended
High Confidence Match
The model predicts the user will engage with this content based on strong tag matching and tech watch history.
Confidence Level
High (92%)
Decision Verdict
Recommended
Raw Score
0.92
Contributing Features
3 Active
Contributing Feature Signals
Weighted normalized sub-scores
2D Decision Boundary Space (Affinity vs Watch Time)
Recommended
Not Recommended
Current Video
Points located above or to the right of the decision threshold boundary cross into the recommendation zone.
3. Beginner AI / ML Concepts Explained
Core principles from the source learning journey
What is Artificial Intelligence?
AI is a broad technology field that allows computers to perform tasks that typically require human cognitive ability:
- Recognizing images and objects
- Understanding human speech and natural language
- Making data-driven decisions and solving complex problems
- Recommending content tailored to individual interests
What is Machine Learning?
Machine Learning is a specialized branch of AI. Instead of manually writing if-else rules for every scenario, we train algorithms on historical data:
- Features: Measured properties like watch time, tags, and previous history.
- Weights: How much importance the model assigns to each signal.
- Decision Boundary: The threshold separating choices (e.g. recommend vs skip).
How Video Recommendations Work
When you watch videos online, the platform evaluates thousands of candidate videos in real time:
- Calculates category affinity from your recent watch log
- Measures tag overlap between your likes and the video
- Weights your average watch duration to predict retention
- Ranks all candidates and serves the top scoring matches
Source & Reference Grounding:
Based on Sankalp Satpute's educational post "My First Step into AI and Machine Learning":
https://sankalpsatpute.medium.com/my-first-step-into-ai-and-machine-learning-b046eed392a6.
Educational sandbox model simulates linear weighted feature combination:
Score = w_tag · TagMatch + w_aff · Affinity + w_dur · Duration followed by threshold classification.