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Coursera - Programming Exercise - Machine Learning by Stanford University
Sendo feito em Octave 6.2.0, pois o Professor Andrew Ng percebeu que quando usava Octave os alunos entendiam mais a matéria, mas tenho objetivo de refazer em Python.
Programming Exercise 1 - Linear Regression
In this part of this exercise, you will implement linear regression with one variable to predict profits for a food truck. Suppose you are the CEO of a restaurant franchise and are considering different cities for opening a new outlet. The chain already has trucks in various cities and you have data for profits and populations from the cities. You would like to use this data to help you select which city to expand to next.
Exercise Part
Exercise
Submitted File
Done
1
Warm up exercise
warmUpExercise.m
2
Compute cost for one variable
computeCost.m
3
Gradient descent for one variable
gradientDescent.m
Training Data
Training Linear Regression with One Variable Result
Surface
Contour with minimun(X)
Exercise Part
Exercise
Submitted File
Done
4
Feature normalization
featureNormalize.m
5
Compute cost for multiple variables
computeCostMulti.m
6
Gradient descent for multiple variables
gradientDescentMulti.m
7
Normal equations
normalEqn.m
Programming Exercise 2 - Logistic Regression
In this part of the exercise, you will build a logistic regression model to predict whether a student gets admitted into a university. Suppose that you are the administrator of a university department and you want to determine each applicant's chance of admission based on their results on two exams. You have historical data from previous applicants that you can use as a training set for logistic regression. For each training example, you have the applicant's scores on two exams and the admissions decision. Your task is to build a classification model that estimates an applicant's probability of admission based the scores from those two exams.
Training Data
Non Regularized Logistic Regression Decision Boundary
Exercise Part
Exercise
Submitted File
Done
1
Sigmoid function
sigmoid.m
2
Compute cost for logistic regression
costFunction.m
3
Gradient for logistic regression
costFunction.m
4
Predict function
predict.m
Training Data
Regularized Logistic Regression Decision Boundary
Underfitting
Overfitting
Exercise Part
Exercise
Submitted File
Done
5
Compute cost for regularized LR
costFunctionReg.m
6
Gradient for regularized LR
costFunctionReg.m
Programming Exercise 3 - Multi-class Classification and Neural Networks
In this exercise, you will implement one-vs-all logistic regression and neural networks to recognize hand-written digits.
Training Data Sample (100 from 5000 images)
Exercise Part
Exercise
Submitted File
Done
1
Regularized logistic regression
lrCostFunction.m
2
One-vs-all classifier training
oneVsAll.m
3
One-vs-all classifier prediction
predictOneVsAll.m
In this part of the exercise, you will implement a neural network to recognize handwritten digits using the same training set as before. The neural network will be able to represent complex models that form non-linear hypotheses. For this week, you will be using parameters from a neural network that we have already trained. Your goal is to implement the feedforward propagation algorithm to use our weights for prediction.
Exercise Part
Exercise
Submitted File
Done
4
Neural network prediction function
predict.m
Octave doesn't have 0 index, so we are using 10 to index the number 0.
GIF
Programming Exercise 4 - Neural Networks Learning
In this exercise, you will implement the backpropagation algorithm for neural networks and apply it to the task of hand-written digit recognition.
Exercise Part
Exercise
Submitted File
Done
1
Feedforward and Cost Function
nnCostFunction.m
2
Regularized Cost Function
nnCostFunction.m
3
Sigmoid Gradient
sigmoidGradient.m
4
Neural Net Gradient Function (Backpropagation)
nnCostFunction.m
5
Regularized Gradient
nnCostFunction.m
Programming Exercise 5 - Regularized Linear Regression and Bias v.s. Variance
In this exercise, you will implement regularized linear regression and use it to study models with diferent bias-variance properties.
Training Data
Exercise Part
Exercise
Submitted File
Done
1
Regularized Linear Regression Cost Function
linearRegCostFunction.m
2
Regularized Linear Regression Gradient
linearRegCostFunction.m
3
Learning Curve
learningCurve.m
Linear Fit
Linear Regression Learning Curve
Underfitting
High Bias Problem (Underfit)
Exercise Part
Exercise
Submitted File
Done
4
Polynomial Feature Mapping
polyFeatures.m
Using Polynomial Feature Mapping to make the model more complex.
Polynomial Fit - lambda = 0
Polynomial Learning Curve
Overfitting
High Variance Problem (Overfit)
Polynomial Fit - lambda = 3
Polynomial Learning Curve
Good Fit
Low Variance and Bias
Exercise Part
Exercise
Submitted File
Done
5
Cross Validation Curve
validationCurve.m
Programming Exercise 6: Support Vector Machines
In this exercise, you will be using support vector machines (SVMs) to build a spam classifier.
Exercise Part
Exercise
Submitted File
Done
1
Gaussian Kernel
gaussianKernel.m
2
Parameters (C, σ) for Dataset 3
dataset3Params.m
3
Email Preprocessing
processEmail.m
4
Email Feature Extraction
emailFeatures.m
Programming Exercise 7 - K-means Clustering and Principal Component Analysis
In this exercise, you will implement the K-means clustering algorithm and apply it to compress an image. In the second part, you will use principal component analysis to find a low-dimensional representation of face images.
Exercise Part
Exercise
Submitted File
Done
1
Find Closest Centroids
findClosestCentroids.m
2
Compute Centroid Means
computeCentroids.m
K-means iteration GIF
Image compression with K-means
In this exercise, you will use principal component analysis (PCA) to perform dimensionality reduction. You will first experiment with an example 2D dataset to get intuition on how PCA works, and then use it on a bigger dataset of 5000 face image dataset.
Exercise Part
Exercise
Submitted File
Done
3
PCA
pca.m
Dataset with Computed eigenvectors
Exercise Part
Exercise
Submitted File
Done
4
Project Data
projectData.m
5
Recover Data
recoverData.m
Projected(red) and Reconstructed(blue) data
PCA on the face dataset
Original and Reconstructed Images
Programming Exercise 8 - Anomaly Detection and Recommender Systems
In this exercise, you will implement the anomaly detection algorithm and apply it to detect failing servers on a network. In the second part, you will use collaborative ltering to build a recommender system for movies.
Training Data
Training Data with Gaussian Estimation Contours
Exercise Part
Exercise
Submitted File
Done
1
Estimate Gaussian Parameters
estimateGuassian.m
2
Select Threshold
selectThreshold.m
Detected Anomaly
In this part of the exercise, you will implement the collaborative filtering learning algorithm and apply it to a dataset of movie ratings.
