AI & Machine Learning Basics

What are AI and ML?

Artificial Intelligence (AI) is a broad field of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence.

Machine Learning (ML) is a subset of AI that focuses on the development of algorithms that allow computers to learn from and make predictions or decisions based on data, without being explicitly programmed.

Key Concepts in Machine Learning

• Data: The fuel for ML algorithms. The quality and quantity of data significantly impact performance.
• Features: Measurable characteristics or attributes of the data used as input for ML models.
• Models: Mathematical representations learned from data that can make predictions or decisions.
• Training: The process of feeding data to an ML algorithm to learn patterns and adjust its parameters.
• Testing/Evaluation: Assessing the performance of a trained model on unseen data.

Types of Machine Learning

Machine Learning can be broadly categorized into:

• Supervised Learning: The algorithm learns from labeled data (input-output pairs).
  • Classification: Predicting a categorical label (e.g., spam or not spam).
  • Regression: Predicting a continuous value (e.g., house price).
• Unsupervised Learning: The algorithm learns from unlabeled data, finding patterns and structures.
  • Clustering: Grouping similar data points together.
  • Dimensionality Reduction: Reducing the number of features while preserving important information.
• Reinforcement Learning: The algorithm learns by interacting with an environment, receiving rewards or penalties for its actions.

A Simple Example: Linear Regression

Linear regression is a fundamental supervised learning algorithm used for predicting a continuous output variable based on one or more input features. It models the relationship between variables by fitting a linear equation to the observed data.

The equation is generally represented as:

where:

• y is the predicted output.
• x1, x2, ..., xn are the input features.
• b0 is the intercept (bias).
• b1, b2, ..., bn are the coefficients for each feature.

A very simple case is Simple Linear Regression with one feature:

We aim to find the values of b0 and b1 that minimize the difference between the predicted values and the actual values in the training data.

Getting Started

To begin your journey, consider exploring these popular libraries:

• Scikit-learn: A comprehensive library for classical ML algorithms in Python.
• TensorFlow & PyTorch: Powerful libraries for deep learning and neural networks.

Practice is key! Try working through tutorials and simple projects to solidify your understanding.

Ready to dive deeper? Check out our Deep Learning Introduction.