Lesson Progress
0% Complete
🗂️ 1. Data Collection
- What it is: The foundation of AI. Gathering the right data for the problem you want to solve.
- Types of Data:
- Structured (organized in tables — e.g., transaction logs, sensor readings).
- Unstructured (text, images, videos, audio).
- Why it matters:
- Quality (clean, accurate, relevant data) reduces errors.
- Quantity (enough examples) helps the AI learn patterns reliably.
- Example: Collecting thousands of labeled images of cats and dogs, or customer purchase histories to predict buying trends.
🏋️ 2. Training
- What it is: Feeding the collected data into an algorithm so the AI can “learn.”
- How it works:
- Algorithms detect patterns and relationships in the data.
- Over time, the system adjusts its internal parameters to minimize errors.
- The result is a trained model that can make predictions.
- Example: The AI learns that cats usually have pointy ears and smaller noses, while dogs often have longer snouts.
📊 3. Evaluation
- What it is: Testing how well the trained model performs using new, unseen data (not used during training).
- Key Metrics: Accuracy, precision, recall, F1 score, fairness, and bias.
- Why it matters: Prevents overfitting (when the AI memorizes training data but fails on new cases).
- Example: Showing the model new photos it has never seen before, and checking if it correctly identifies cats vs. dogs.
🚀 4. Deployment
- What it is: Putting the trained model into a real-world application.
- How it works:
- Integrated into apps, websites, or devices.
- Continuously monitored to ensure performance remains high.
- Feedback Loop: AI systems often keep learning by collecting new data and retraining.
- Example: A mobile app that recognizes your pet’s breed from a photo — and improves over time as more users upload images.
✅ In short:
- Collect good data → 2. Train the AI → 3. Evaluate performance → 4. Deploy & improve.
The cycle is continuous, as feedback and new data make AI smarter over time.
