Lesson Progress
0% Complete
🔑 1. Algorithms in AI
An algorithm is a step-by-step set of rules or instructions that an AI system follows to solve a problem or make a decision.
- Think of it as the recipe that tells the AI what to do with data.
Common AI Algorithms:
- Decision Trees: Break data into branches to make predictions (used in credit scoring, medical diagnosis).
- k-Nearest Neighbors (k-NN): Predicts by comparing new data to the most similar past data points.
- Support Vector Machines (SVM): Separates data into categories with a decision boundary.
- Neural Networks: Algorithms inspired by the human brain that recognize complex patterns (used in image and speech recognition).
- Reinforcement Learning Algorithms: Learn by trial and error, using rewards and penalties (used in robotics, gaming, self-driving cars).
🔑 2. Models in AI
A model is the result of training an algorithm on data — it represents what the AI has “learned.”
- If an algorithm is the recipe, the model is the finished dish.
- Models can then make predictions or decisions on new, unseen data.
Types of AI Models:
- Supervised Learning Models
- Trained on labeled data (input + correct answer).
- Example: Predicting house prices based on features like size and location.
- Unsupervised Learning Models
- Find hidden patterns in unlabeled data.
- Example: Customer segmentation (grouping buyers with similar behavior).
- Reinforcement Learning Models
- Learn by interacting with an environment and receiving rewards or penalties.
- Example: AI learning to play chess or optimize warehouse robots.
- Deep Learning Models
- A subset of neural networks with many layers (“deep”).
- Example: Facial recognition, self-driving car vision systems.
⚡ How They Work Together
- Algorithm → Defines the process (e.g., “find the best line that separates cats from dogs”).
- Data → Feeds examples into the algorithm.
- Model → The trained system that can now recognize cats vs. dogs in new images.
✅ In simple terms:
- Algorithms are the methods (how AI learns).
- Models are the outcomes (what AI has learned).
