Machine Learning

Reference

• Taught by: Andrew Ng, Associate Professor, Stanford University
• Site: https://www.coursera.org/learn/machine-learning

Contents

• WEEK 1 : What's Machine Learning?

  • Introduction
  • What is Machine Learning?
  • Supervised Learning & Unsupervised Learning
  • Model and Cost Function
  • Model Representation
  • Cost Function
  • Parameter Learning
  • Gradient Descent
  • Gradient Descent for Linear Regression

• WEEK 2 : Parameters Learning

  • Multivariate Linear Regression
  • Multiple Features
  • Gradient Descent for Multiple Variables
  • Feature Scaling
  • Features and Polynomial Regression
  • Computing Parameters Analytically
  • Normal Equation

• WEEK 3 : Classification and Logistic Regression

  • Classification and Representation
  • Hypothesis Representation
  • Decision Boundary
  • Logistic Regression Model
  • Cost Function
  • Simplified Cost Function and Gradient Descent
  • Advanced Optimization
  • Multi-class Classification
  • One vs all
  • Solving the Problem of Overfitting
  • The Problem of Overfitting
  • Regularization: Cost Function
  • Regularized Linear Regression and Logistic Regression

• WEEK 4 : Neural Networks

  • Neural Networks: Representation
  • Neuron Model
  • Neural Networks
  • Multi-class Classification

• WEEK 5 : Neural Networks

  • Neural Networks: Cost Function and Backpropagation
  • Cost Function
  • Parameters Learning: Backpropagation Algorithm
  • Gradient Checking and Random Initialization

• WEEK 6 : Advice for Applying Machine Learning

  • Advice for Applying Machine Learning
  • Deciding What to Try Next
  • Evaluating a Hypothesis
  • Model Selection and Train/Validation/Test Sets
  • Diagnosing Bias vs. Variance
  • Regularization and Bias/Variance
  • Learning Curves
  • Deciding What to Try Next Revisited
  • Machine Learning System Design
  • Precision and Recall
  • Trading off Precision and Recall
  • Data for Machine Learning