Machine-Learning-Coursera

Machine Learning - Stanford University.

Taught by Prof. Andrew Ng.

Verified Certificate

Syllabus:

Week 1: Introduction

[Slides 1-3][Lecture Notes]

• Supervised/Unsupervised Learning
• Linear Regression with One Variable
• Gradient Descent for Linear Regression
• Linear Algebra Review

Week 2: Linear Regression with Multiple Variables

[Slides 4-5][Lecture Notes][Assignment]

• Multivariate Linear Regression
• Normal Equation
• Octave/Matlab basic tutorial

Week 3: Logistic Regression

[Slides 6-7][Lecture Notes][Assignment]

• Classification
• Logistic Regression Model
• Multiclass Classification
• Overfitting
• Regularization

Week 4: Neural Network Representation

[Slides 8][Lecture Notes][Assignment]

• Model Representation
• Multiclass Classification

Week 5: Neural Network Learning

[Slides 9][Lecture Notes][Assignment]

• Foward Propagation
• Backward Propagation
• Gradient Checking
• Random Initalization

Week 6: Aviced for Applying Machine Learning

[Slides 10-11][Lecture Notes][Assignment]

• Evaluating a hypothesis
• Bias vs Variance
• Regularization
• Error Analysis
• Handling Skewed Data
• Spam Classifier

Week 7: Support Vector Machine

[Slides 12][Lecture Notes][Assignment]

• Large Margin Classification with SVM
• Kernels

Week 8: Unsupervised Learning

[Slides 13-14][Lecture Notes][Assignment]

• Clustering: K-means Algorithms
• Dimensionality Reduction: Principal Component Analysis (PCA)

Week 9: Anomaly Detection

[Slides 15-16][Lecture Notes][Assignment]

• Density Estimation
• Anomaly Detection
• Recommender System

Week 10: Large Scale Machine Learning

[Slides 17][Lecture Notes]

• Stochastic Gradient Descent
• Mini-batch Gradient Descent
• Online Learning

Week 11: Photo OCR

[Slides 18]

• Sliding Window
• Machine Learning pipeline