MLG 005 Linear Regression

Machine Learning Guide

Try a walking desk to stay healthy while you study or work!

Show notes at ocdevel.com/mlg/5. See Andrew Ng Week 2 Lecture Notes

Key Concepts

• Machine Learning Hierarchy: Explains the breakdown into supervised, unsupervised, and reinforcement learning with an emphasis on supervised learning, which includes classification and regression.
• Supervised Learning: Divided into classifiers and regressors, with this episode focusing on linear regression as an introduction to regressor algorithms.
• Linear Regression: A basic supervised algorithm used for estimating continuous numeric outputs, such as predicting housing prices.

Process of Linear Regression

1. Prediction: Using a hypothesis function, predictions are made based on input features.
2. Evaluation: Implements a cost function, "mean squared error," to measure prediction accuracy.
3. Learning: Employs gradient descent, which uses calculus to adjust and minimize error by updating weights and biases.

Concepts Explored

• Univariate vs. Multivariate Linear Regression: Focus on a single predictive feature versus multiple features, respectively.
• Gradient Descent: An optimization technique that iteratively updates parameters to minimize the cost function.
• Bias Parameter: Represents an average outcome in absence of specific feature information.
• Mean Squared Error: Common cost function used to quantify the error in predictions.

Resources

• Andrew Ng's Coursera Course: A highly recommended resource for comprehensive and practical learning in machine learning. Course covers numerous foundational topics, including linear regression and more advanced techniques.

Access to Andrew Ng's Course on Coursera is encouraged to gain in-depth understanding and application skills in machine learning.

Coursera: Machine Learning by Andrew Ng