The Science of Artificial Intelligence and Machine Learning

Brandon John Grenier

Series: An Introduction to Artificial Intelligence and Machine Learning Algorithms

This series is intended for readers who are interested in a technical introduction to the foundational mathematical algorithms used in Artificial Intelliengce and Machine Learning. No prior knowledge or experience in Artificial Intelligence or Machine Learning is required. Although there is a fair amount of math to work through, knowledge of mathematics at a high school level will suffice.

Chapter 1: An Introduction to Artificial Intelligence and Machine Learning
What is Artificial Intelligence?
What is Machine Learning?
Machine Learning Algorithmic Strategies
Machine Learning Predictive Models
Chapter 2: Linear Regression with One Feature
Intuitive Linear Regression
The Hypothesis Function
The Cost Function
The Gradient Descent Algorithm
Linear Regression Summary
Chapter 3: Linear Regression with Multiple Features
The Hypothesis Function
The Cost Function
The Gradient Descent Algorithm
Feature Scaling
Chapter 4: Polynomial Regression
Chapter 5: Logistic Regression
Chapter 6: Support Vector Machines
Chapter 7: K-Means Clustering
Chapter 8: Principal Component Analysis
Chapter 9: Neural Networks
Chapter 10: Anomaly Detection
Chapter 11: Recommender Systems
Appendix A: Linear Functions Review

Series: An Introduction to Deep Learning

 

Series: An Introduction to Reinforcement Learning