AI

 Artificial Intelligence (AI) is a branch of computer science that focuses on creating systems that can perform tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, and natural language understanding.

1. AI vs. Machine Learning vs. Deep Learning

Concept	Definition	Example
Artificial Intelligence (AI)	The broad field of making machines "intelligent"	Chatbots, self-driving cars
Machine Learning (ML)	A subset of AI where machines learn from data to make predictions	Spam detection, product recommendations
Deep Learning (DL)	A subset of ML that uses artificial neural networks for complex tasks	Face recognition, language translation

2. TYPES OF AI LEARNING

A. Supervised Learning -- The model is trained on labeled data 

B. Unsupervised Learning -- The model learns patterns from unlabeled data.

C. Reinforcement Learning -- The model learns by trial and error, receiving rewards for good actions.

3. Key Components of ML

• Dataset: Collection of examples (inputs and outputs).

• Features (X): Input variables used for predictions.

• Target (Y): Output variable (what we want to predict).

• Model: Mathematical representation of learned patterns.

• Training: The process of finding patterns in data.

• Evaluation: Testing the model on unseen data to measure accuracy

  
  

4. Machine Learning Workflow

1. Data Collection → Gather and clean data.

2. Feature Engineering → Select and preprocess input variables.

3. Model Selection → Choose an ML algorithm (e.g., Decision Trees, Neural Networks).

4. Training → Train the model on historical data.

5. Evaluation → Test model performance on unseen data.

6. Deployment → Use the model in real-world applications.