Definition: Acquiring skills through experience and observations.

Observations → Learning → Skill

Process:

Explicit Learning (Conscious, structured learning)

Implicit Learning (Unconscious, experience-based)

Associative Learning (Classical & Operant Conditioning)

Observational Learning (Learning by watching others)

Experiential Learning (Learning by doing)

Problem-Based Learning (Solving real-world problems)

Types of Learning:

1. Human Learning

Definition: Acquiring skills through experience accumulated/computed from data.

Data → ML Algorithm → Skill

Process:

Supervised Learning (Labeled data, predicting outcomes)

Unsupervised Learning (Unlabeled data, finding patterns)

Reinforcement Learning (Learning through rewards and penalties)

Types of Machine Learning:

Data (Raw observations)

Algorithms (Models that process data)

Training & Testing (Evaluating performance)

Optimization (Improving learning process)

Key Components:

2. Machine Learning (ML)

Intuition, creativity, reasoning.

Adapts to new environments with minimal data.

Learns from emotions and social interactions.

Human Learning:

Relies on structured data and algorithms.

Needs large amounts of data to generalize.

Performs repetitive tasks with high accuracy.

Machine Learning:

3. Key Differences Between Human and Machine Learning

Definition: The ability to apply knowledge effectively.

Cognitive Skills (Logical thinking, problem-solving)

Motor Skills (Physical tasks like writing, driving)

Social Skills (Communication, teamwork)

Types of Skills:

In Machine Learning:

4. What is Skill?

From Learning to Machine Learning