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Machine Learning Course by Stanford on Coursera (Andrew Ng)

ml-stanford 최신버젼 다운로드

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# Machine Learning ~ Stanford

![made-with-matlab](https://img.shields.io/badge/Made%20with-MATLAB-1f425f.svg)
![problems-solved](https://img.shields.io/badge/Problems%20Solved-100%25-1abc9c.svg)
[![course-list](https://img.shields.io/badge/also%20see-Other%20Coursera%20Courses-1f72ff.svg)](https://github.com/anishLearnsToCode/course-list)

## 📖 Overview
- [Week 1](#week-1)
- [Week 2](#week-2)
- [Week 3](#week-3)
- [Week 4](#week-4)
- [Week 5](#week-5)
- [Week 6](#week-6)
- [Week 7](#week-7)
- [Week 8](#week-8)
- [Week 9](#week-9)
- [Week 10](#week-10)
- [Week 11](#week-11)
- [Certificate](#-certificate)

## Week 1
### Quizzes
- [Introduction](week1/introduction.pdf)
- [Linear Regression With One Variable](week1/linear-regression-one-variable.pdf)
- [Linear Algebra](week1/linear-agebra.pdf)

## Week 2
### Quizzes
- [Linear Regression With Multiple Variables](week2/linear-regression-multiple-variables.pdf)
- [Octave/Matlab Tutorial](week2/octave-matlab-tutoria.pdf)

### Programming Exercises
- [Questions and Explanation](week2/ex1.pdf)
- [Exercise 1](week2/ex1)
    - [Warm Up Exercise](week2/ex1/warmUpExercise.m)
    - [Compute Cost for One Variable](week2/ex1/computeCost.m)
    - [Compute Cost For Multiple Variables](week2/ex1/computeCostMulti.m)
    - [Gradient Descent For One Variable](week2/ex1/gradientDescent.m)
    - [Gradient Descent For Multiple Variables](week2/ex1/gradientDescentMulti.m)

## Week 3
### Quizzes
- [Logistic Regression](week3/logistic-regression-quiz.md)
- [Regularization](week3/regularization-quiz.md)

### Programming Exercises
- [Questions](week3/ex2.pdf) 
- [Exercise 2](week3/ex2)
    - [Sigmoid Function](week3/ex2/sigmoid.m)
    - [Logistic Regression Cost](week3/ex2/costFunction.m)
    - [Logistic Regression Gradient](week3/ex2/costFunction.m)
    - [Regularized Logistic Regression Cost](week3/ex2/costFunctionReg.m)
    - [Regularized Logistic Regression Gradient](week3/ex2/costFunctionReg.m)
    - [Predict](week3/ex2/predict.m)

## Week 4 
### Quizzes
- [Neural Networks: Representation](week4/neural-networks-quiz.md)

### Programming Exercises
- [Questions and Explanations](week4/machine-learning-ex3/ex3.pdf)
- [Exercise 3](week4/machine-learning-ex3/ex3)
    - [Logistic Regression Cost Function](week4/machine-learning-ex3/ex3/lrCostFunction.m)
    - [One vs. All Multi Class Classifier](week4/machine-learning-ex3/ex3/oneVsAll.m)
    - [Predict one vs. all Multi Class Classifier](week4/machine-learning-ex3/ex3/predictOneVsAll.m)
    - [Neural Network Prediction Function](week4/machine-learning-ex3/ex3/predict.m)

## Week 5
### Quizzes
- [Neural Networks: Learning](week5/neural-networks-quiz.md)

### Programming Exercises
- [Questions and Explanations](week5/ex4.pdf)
- [Exercise 4](week5/ex4)
    - [Feedforward and Cost Function](week5/ex4/nnCostFunction.m)
    - [Regularized Cost Function](week5/ex4/nnCostFunction.m)
    - [Compute Gradient of Sigmoid Function](week5/ex4/sigmoidGradient.m)
    - [Randomly Initialize Weights](week5/ex4/randInitializeWeights.m)
    - [Neural Network Gradient (Backpropagation)](week5/ex4/checkNNGradients.m) 
    - [Neural Network Cost Function](week5/ex4/nnCostFunction.m)
    - [Regularized Gradient](week5/ex4/checkNNGradients.m)

## Week 6
### Quizzes
- [Advice for Applied Machine Learning](week6/advice-for-applying-machine-learning.md)
- [Machine  Learning System Design Quiz](week6/machine-learning-system-design-quiz.md)

### Programming Exercises
- [Questions and Explanations](week6/ex5.pdf)
- [Exercise 5](week6/ex5)
    - [Regularized Linear Regression Cost Function](week6/ex5/linearRegCostFunction.m)
    - [Generate Learning Curve](week6/ex5/learningCurve.m)
    - [Maps Data into Polynomial Feature Space](week6/ex5/polyFeatures.m)
    - [Generates a Cross Validation Curve](week6/ex5/validationCurve.m)

## Week 7
### Quizzes
- [Support Vector Machines (SVM)](week7/support-vector-machines-quiz.md)

### Programming Exercises
- [Questions and Explanations](week7/ex6.pdf)
- [Exercise 6: Support Vector Machines](week7/ex6)
    - [Gaussian Kernel for SVM](week7/ex6/gaussianKernel.m)
    - [Parameters to Use for Dataset 3](week7/ex6/dataset3Params.m)
    - [Email Preprocessing](week7/ex6/processEmail.m)
    - [Feature Extraction From Email](week7/ex6/emailFeatures.m)

## Week 8
### Quizzes
- [Unsupervised Learning](week8/unsupervised-learning-quiz.md)
- [Principal Component Analysis](week8/principal-component-analysis.md)

### Programming Exercises
- [Exercise 7: Questions and Explanations](week8/ex7.pdf)
- [K Means Clustering and PCA(Principal Component Analysis)](week8/ex7)
    - [Perform PCA(Principal Component Analysis)](week8/ex7/pca.m)
    - [Project a Dataset into lower dimensional space](week8/ex7/projectData.m)
    - [Recover the Original Data from the Projection](week8/ex7/recoverData.m)
    - [Find Closest Centroids Using K-Means](week8/ex7/findClosestCentroids.m)
    - [Compute Centroid Means](week8/ex7/computeCentroids.m)
    - [Initialize K means for Centroids](week8/ex7/kMeansInitCentroids.m)

## Week 9
### Quizzes
- [Anomaly Detection](week9/anomaly-detection-quiz.md)
- [Reccomender Systems](week9/reccomender-systems-quiz.md)

### Programming Exercises
- [Exercise 8: Questions and Explanations](week9/ex8.pdf)
- [Anomaly Detection and Reccomender Systems](week9/ex8)
    - [Estimate Gaussian Parameters](week9/ex8/estimateGaussian.m)
    - [Find Threshold for Anomaly Detection](week9/ex8/selectThreshold.m)
    - [Cost Function for Collaborative Filterinf](week9/ex8/cofiCostFunc.m)

## Week 10
### Quizzes
- [Large Scale Machine Learning](week10/large-scale-ml-quiz.md)

## Week 11
### Quizzes
- [Application: Photo OCR](week11/application-photo-ocr-quiz.md)

## 🎓 [Certificate](http://coursera.org/verify/PY3HEUJFNZ2M)
![certificate](https://raw.githubusercontent.com/anishLearnsToCode/ml-stanford/master/assets/certificate.PNG)

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