Lesson 1.1: The Core AI Paradigm

Traditional software relies on explicit rules written by developers (If X happens, do Y). Artificial Intelligence flips this model: instead of writing explicit code for every scenario, we feed algorithms data and outputs, allowing the machine to learn the underlying rules automatically.

Lesson 1.2: The Three Levels of AI

• Artificial Narrow Intelligence (ANI): Specialized AI engineered for a single task (e.g., Google Maps, spam filters, chess bots). All present-day AI belongs to this stage.
• Artificial General Intelligence (AGI):** A theoretical system possessing human-level adaptability across any intellectual domain.
• Artificial Superintelligence (ASI):** A hypothetical intelligence surpassing all human cognitive abilities combined.

Lesson 2.1: The 4-Part Prompt Architecture

Structure your LLM prompts using this consistent pattern for optimal accuracy:

Lesson 2.2: Advanced Prompting Techniques

1. Few-Shot Prompting: Provide 2–3 exemplar input-output pairs inside the prompt to enforce strict patterns.

2. Chain-of-Thought (CoT) Prompting: Instruct the model to “think step-by-step” prior to generating answers to eliminate multi-step math and logic errors.

Lesson 3.1: Retrieval-Augmented Generation (RAG)

LLMs cannot access internal private documents out of the box. RAG solves this by retrieving relevant document snippets from a database and inserting them directly into the prompt context.

Lesson 4.1: Linear Algebra & Gradient Descent

AI models treat data as multi-dimensional vectors. Optimization is handled via Gradient Descent to minimize prediction error $J( heta)$:

Lesson 5.1: Supervised Learning & Sigmoid Function

Binary classification models use the Sigmoid function σ(z) to convert outputs into probabilities between 0 and 1:

Lesson 5.2: Python Code Example

Below is a runnable Python example showing model training using scikit-learn:

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# 1. Load Data & Split
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 2. Train Model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# 3. Evaluate
preds = model.predict(X_test)
print(f"Model Accuracy: {accuracy_score(y_test, preds) * 100:.2f}%")

Lesson 6.1: Scaled Dot-Product Attention

Modern Large Language Models process tokens using the Attention mechanism from “Attention Is All You Need”: