Rawkush/ML-notes

machine learning notes made from andrew ng course

ML Notes

This repository contains my handwritten notes on Machine Learning Originally written as a way for me personally to help in understanding the concepts, these notes have lot of diagrams taken from the Andrew Ng course! The target audience was originally me, but anyone is welcome to use it.

Content

• 01: Introduction to ML, Regression Analysis and Gradient Descent

• 02: Linear Algebra - review

• 04: Linear Regression with Multiple Variables

• 05: Learning Octave

• 06: Logistic Regression

• 07: Regularization

• 08: Neural Networks - Representation

• 09: Neural Networks - Learning

• 10: Advice for applying machine learning techniques

• 11: Machine Learning System Design

• 12: Support Vector Machines

• 13: Clustering

• 14: Dimensionality Reduction

• 15: Anomaly Detection

• 16: Recommender Systems

• 17: Large Scale Machine Learning

• 18: Application Example - Photo OCR

Course Summary

• Supervised learning - labeled data

  • Linear regression
  • Logistic regression
  • Neural networks
  • Support vector machines

• Unsupervised learning - unlabeled data

  • K-means
  • PCA
  • Anomaly detection

• Special applications/topics

  • Recommender systems
  • Large scale machine learning

• Advice on building machine learning systems

  • Bias and variance
  • Regularization
  • What to do next when developing a system
  • Algorithm evaluation
  • Learning curves
  • Error analysis
  • Ceiling analysis