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AI Class 10 Code 417 - Coggle Diagram
AI Class 10 Code 417
Modelling
AI Taxonomy
Artificial Intelligence
Machine Learning
Deep Learning
Model Approaches
Rule-Based
Developer rules
Static system
Fails on edge cases
Learning-Based
Learns patterns
Finds trends
Supervised Learning
Labelled data
Classification
Discrete categories
Yes/No
Regression
Continuous values
Price/temperature
Unsupervised Learning
Unlabelled data
Clustering
Similarity groups
Association
Co-occurrence rules
Dimensionality Reduction
Feature compression
Reinforcement Learning
Trial and error
Rewards
Penalties
Artificial Neural Networks
Input Layer
Hidden Layers
Output Layer
Weights
Biases
Activation Threshold
Feature extraction
Efficiency Pitfalls
Overfitting
High training accuracy
Low testing accuracy
Memorizes noise
Underfitting
Inadequate learning
Poor accuracy
AI Project Cycle
6 Stages
Problem Scoping
Data Acquisition
Data Exploration
Modelling
Evaluation
Deployment
Problem Scoping
4Ws Canvas
Who
What
Where
Why
Problem Statement
Data Acquisition
Train-Test Split
Training → Learning
Testing → Validation
Ethical Sourcing
Consent
Privacy
Authentic sources
Data Exploration
Handle null values
CSV formatting
Visualize distributions
Deployment
Real-world rollout
Introduction to AI & Ethics
AI Definition
Learns from data
Adapts to situations
Makes decisions
AI vs Non-AI
9 Intelligences
Mathematical-Logical
Linguistic
Spatial
Kinesthetic
Musical
Intrapersonal
Interpersonal
Naturalist
Existential
3 AI Domains
Statistical Data
Computer Vision
NLP
Ethical Frameworks
Bioethics
Autonomy
Non-maleficence
Beneficence
Justice
Value-Based
Rights-based
Utility-based
Virtue-based
Ethical Risks
Algorithmic bias
Data privacy
Model Evaluation
Prediction vs Reality
Prediction → Model output
Reality → Actual result
Confusion Matrix
True Positive
Actual 1
Predicted 1
True Negative
Actual 0
Predicted 0
False Positive
Actual 0
Predicted 1
Type I Error
False Negative
Actual 1
Predicted 0
Type II Error
Evaluation Metrics
Accuracy
(TP + TN) / Total
Imbalanced data limitation
Precision
TP / (TP + FP)
False Positive focus
Recall
TP / (TP + FN)
False Negative focus
F1 Score
2 × Precision × Recall
Balanced metric
Evaluation Ethics
Transparency
Bias mitigation
Accountability
ICT Skills II
OS Controls
Hover → Preview file info
Double-click → Open/execute
Right-click → Context menu
Hardware Care
Prevent overheating
Unplug after full charge
Data Security
Passwords
Firewalls
Antivirus
HTTPS
Trojan Horse → Disguised malware
Statistics & No-Code AI
Statistical Foundations
Population → All data
Sample → Data subset
Central Tendency
Mean → Average
Median → Midpoint
Mode → Most frequent
Data Spread
Variance
Standard deviation
Outliers
Distributions
Normal → Bell curve
Skewed → Asymmetric
Code Approaches
High-Code
Low-Code
No-Code
Visual GUI
Orange Data Mining
Data Widgets
File
CSV Import
Data Table
Transform Widgets
Impute
Select Columns
Visualize Widgets
Scatter Plot
Distributions
Box Plot
Model Widgets
Linear Regression
Logistic Regression
Tree
Evaluation Widgets
Test and Score
Confusion Matrix
ROC Analysis