Machine Learning Course

This structure and all of this original content is the work of Prof. Andrew Ng of Stanford University, Co-founder of Coursera

You can learn by yourself at Machine Learning Course at Coursera

Cấu trúc và các nội dung gốc của tất cả các bài viết dưới đây do giáo sư Andrew Ng xây dựng. Ông là giáo sư của đại học Stanford, đồng sáng lập Coursera

Bạn có thể tự học khóa Machine Learning tại Machine Learning Course at Coursera

1. Part 1: Introduction

1.1. Introduction

1.2. Model and Cost Function

1.3. Parameter Learning

1.4. Matrices and Vectors

2. Part 2: Linear Regression with Multiple Variables

2.2. Multivariate Features

2.3. Normal Equation

2.4. Submitting Programming Assignments

2.5. Octave/Matlab Tutorial

3. Part 3: Logistic Regression

3.1. Classification and Representation

3.2. Logistic Regression Model

3.3. Multiclass Classification

3.4. Solving the Problem of Overfitting

4. Part 4: Neural Networks: Representation

4.1. Motivations

4.2. Neural Networks

4.3. Applications

5. Part 5: Neural Networks: Learning

5.1. Cost Function and Backpropagation

5.2. Backpropagation in Practice

5.3. Application of Neural Networks

6. Part 6: Advice for Applying Machine Learning

6.1. Evaluating a Learning Algorithm

6.2. Bias vs. Variance

6.3. Machine Learning System Design

6.4. Building a Spam Classifier

6.5. Handling Skewed Data

6.6. Using Large Data Sets

7. Part 7: Support Vector Machines

7.1. Large Margin Classification

7.2. Kernels

7.3. SVMs in Practice

8. Part 8: Unsupervised Learning

8.1. Clustering

8.2. Dimensionality Reduction

8.3. Motivation

8.4. Principal Component Analysis

8.5. Applying PCA

9. Part 9: Anomaly Detection

9.1. Density Estimation

9.2. Building an Anomaly Detection System

9.3. Multivariate Gaussian Distribution (Optional)

9.4. Predicting Movie Ratings

9.5. Collaborative Filtering

9.6. Low Rank Matrix Factorization

10. Part 10: Large Scale Machine Learning

10.1. Gradient Descent with Large Datasets

10.2. Advanced Topics

11. Part 11: Application Example: Photo OCR

11.1. Photo OCR

Advertisements