Coursera S Machine Learning Notebook

For wrapping up and resume writing
video
Lecture notes
Programming assignment 1

Week 1 Introduction & Linear Regression with One Variable

This method looks at every example in the entire training set on every step, and is called batch gradient descent.

Model and Cost Function

Cost Function

https://www.coursera.org/learn/machine-learning/lecture/rkTp3/cost-function

Cost Function

https://www.coursera.org/learn/machine-learning/supplement/nhzyF/cost-function

Cost Function - Intuition I

https://www.coursera.org/learn/machine-learning/lecture/N09c6/cost-function-intuition-i

Cost Function - Intuition I

https://www.coursera.org/learn/machine-learning/supplement/u3qF5/cost-function-intuition-i

Parameter Learning

Gradient Descent

https://www.coursera.org/learn/machine-learning/lecture/8SpIM/gradient-descent

Gradient Descent

https://www.coursera.org/learn/machine-learning/supplement/2GnUg/gradient-descent

Gradient Descent Intuition

https://www.coursera.org/learn/machine-learning/supplement/QKEdR/gradient-descent-intuition

Gradient Descent For Linear Regression

https://www.coursera.org/learn/machine-learning/supplement/U90DX/gradient-descent-for-linear-regression

Week 2

Feature scaling involves dividing the input values by the range (i.e. the maximum value minus the minimum value) of the input variable, resulting in a new range of just 1.
Mean normalization involves subtracting the average value for an input variable from the values for that input variable resulting in a new average value for the input variable of just zero. To implement both of these techniques, adjust your input values as shown in this formula:
$x_i :=\frac{x_i - \mu_i}{s_i}$
Where $μ_i$ is the average of all the values for feature (i) and $s_i$ is the range of values (max - min), or $s_i$ is the standard deviation.

Linear Regression with Multiple Variables

Environment Setup Instructions

Setting Up Your Programming Assignment Environment8 min

Installing MATLAB3 min

Installing Octave on Windows3 min

Installing Octave on Mac OS X (10.10 Yosemite and 10.9 Mavericks and Later)10 min

Installing Octave on Mac OS X (10.8 Mountain Lion and Earlier)3 min

Installing Octave on GNU/Linux7 min

More Octave/MATLAB resources 10 min

https://www.coursera.org/learn/machine-learning/supplement/Mlf3e/more-octave-matlab-resources

Multivariate Linear Regression

Multiple Features8 min

Multiple Features3 min

Gradient Descent for Multiple Variables5 min

Gradient Descent For Multiple Variables2 min

Gradient Descent in Practice I - Feature Scaling8 min

Gradient Descent in Practice I - Feature Scaling3 min

Gradient Descent in Practice II - Learning Rate8 min

Gradient Descent in Practice II - Learning Rate4 min

Features and Polynomial Regression7 min

Features and Polynomial Regression3 min

Computing Parameters Analytically

Normal Equation16 min

Normal Equation3 min

Normal Equation Noninvertibility5 min

Normal Equation Noninvertibility2 min

Submitting Programming Assignments

Working on and Submitting Programming Assignments3 min

Programming tips from Mentors10 min

Review

Lecture Slides20 min

Quiz: Linear Regression with Multiple Variables5 questions

Octave/Matlab Tutorial

Octave/Matlab Tutorial

Basic Operations13 min

Moving Data Around16 min

Computing on Data13 min

Plotting Data9 min

Control Statements: for, while, if statement12 min

Vectorization13 min

Review

Lecture Slides10 min

The course has ended. Assignments may not be resubmitted.

Quiz: Octave/Matlab Tutorial5 questions

Programming Assignment: Linear Regression3h

Week 3

Logistic Regression

Classification and Representation

Classification8 min

Classification2 min

Hypothesis Representation7 min

Hypothesis Representation3 min

Decision Boundary14 min

Decision Boundary3 min

Logistic Regression Model

Cost Function10 min

Cost Function3 min

Simplified Cost Function and Gradient Descent10 min

Simplified Cost Function and Gradient Descent3 min

Advanced Optimization14 min

Advanced Optimization3 min

Multiclass Classification

Multiclass Classification: One-vs-all6 min

Multiclass Classification: One-vs-all3 min

Review

Lecture Slides 10 min

https://www.coursera.org/learn/machine-learning/supplement/QEYX8/lecture-slides

Quiz: Logistic Regression 5 questions

Regularization

Solving the Problem of Overfitting

The Problem of Overfitting9 min

The Problem of Overfitting3 min

Cost Function10 min

Cost Function3 min

Regularized Linear Regression10 min

Regularized Linear Regression3 min

Regularized Logistic Regression8 min

Regularized Logistic Regression3 min

Review

Lecture Slides10 min

The course has ended. Assignments may not be resubmitted.

Quiz: Regularization5 questions

Programming Assignment: Logistic Regression3h

Week 4

Neural Networks: Representation

Motivations

Non-linear Hypotheses9 min

Neurons and the Brain7 min

Neural Networks

Model Representation I12 min

Model Representation I6 min

Model Representation II11 min

Model Representation II6 min

Applications

Examples and Intuitions I7 min

Examples and Intuitions I2 min

Examples and Intuitions II10 min

Examples and Intuitions II3 min

Multiclass Classification3 min

Multiclass Classification3 min

Review

Lecture Slides10 min

The course has ended. Assignments may not be resubmitted.

Quiz: Neural Networks: Representation5 questions

Programming Assignment: Multi-class Classification and Neural Networks3h

Week 5

Neural Networks: Learning

Cost Function and Backpropagation

Cost Function6 min

Cost Function4 min

Backpropagation Algorithm11 min

Backpropagation Algorithm10 min

Backpropagation Intuition12 min

Backpropagation Intuition4 min

Backpropagation in Practice

Implementation Note: Unrolling Parameters7 min

Implementation Note: Unrolling Parameters3 min

Gradient Checking11 min

Gradient Checking3 min

Random Initialization6 min

Random Initialization3 min

Putting It Together13 min

Putting It Together4 min

Application of Neural Networks

Autonomous Driving6 min

Review

Lecture Slides10 min

https://www.coursera.org/learn/machine-learning/supplement/FklyY/lecture-slides

The course has ended. Assignments may not be resubmitted.

Quiz: Neural Networks: Learning5 questions

Programming Assignment: Neural Network Learning3h

Week 6

Advice for Applying Machine Learning

Evaluating a Learning Algorithm

Deciding What to Try Next5 min

Evaluating a Hypothesis7 min

Evaluating a Hypothesis4 min

Model Selection and Train/Validation/Test Sets12 min

Model Selection and Train/Validation/Test Sets3 min

Bias vs. Variance

Diagnosing Bias vs. Variance7 min

Diagnosing Bias vs. Variance3 min

Regularization and Bias/Variance11 min

Regularization and Bias/Variance3 min

Learning Curves11 min

Learning Curves3 min

Deciding What to Do Next Revisited6 min

Deciding What to do Next Revisited3 min

Review

Lecture Slides 10 min

https://www.coursera.org/learn/machine-learning/supplement/7BHrF/lecture-slides

The course has ended. Assignments may not be resubmitted.

Quiz: Advice for Applying Machine Learning5 questions

Programming Assignment: Regularized Linear Regression and Bias/Variance3h

Machine Learning System Design

Building a Spam Classifier

Prioritizing What to Work On9 min

Prioritizing What to Work On3 min

Error Analysis13 min

Error Analysis3 min

Handling Skewed Data

Error Metrics for Skewed Classes11 min

Trading Off Precision and Recall14 min

Using Large Data Sets

Data For Machine Learning11 min

Review

Lecture Slides 10 min

https://www.coursera.org/learn/machine-learning/supplement/gFC7y/lecture-slides

Quiz: Machine Learning System Design5 questions

Week 7

Support Vector Machines

Large Margin Classification

Optimization Objective14 min

Large Margin Intuition10 min

Mathematics Behind Large Margin Classification19 min

Kernels

Kernels I15 min

Kernels II15 min

SVMs in Practice

Using An SVM21 min

Review

Lecture Slides 10 min

https://www.coursera.org/learn/machine-learning/supplement/pSe2X/lecture-slides

The course has ended. Assignments may not be resubmitted.

Quiz: Support Vector Machines5 questions

Programming Assignment: Support Vector Machines3h

Week 8

Unsupervised Learning

Clustering

Unsupervised Learning: Introduction3 min

K-Means Algorithm12 min

Optimization Objective7 min

Random Initialization7 min

Choosing the Number of Clusters8 min

Review

Lecture Slides 10 min

https://www.coursera.org/learn/machine-learning/supplement/hFF7A/lecture-slides

Quiz: Unsupervised Learning5 questions

Dimensionality Reduction

Motivation

Motivation I: Data Compression10 min

Motivation II: Visualization5 min

Principal Component Analysis

Principal Component Analysis Problem Formulation9 min

Principal Component Analysis Algorithm15 min

Applying PCA

Reconstruction from Compressed Representation3 min

Choosing the Number of Principal Components10 min

Advice for Applying PCA12 min

Review

Lecture Slides 10 min

https://www.coursera.org/learn/machine-learning/supplement/SCJi4/lecture-slides

The course has ended. Assignments may not be resubmitted.

Quiz: Principal Component Analysis5 questions

Programming Assignment: K-Means Clustering and PCA 3h

Week 9

Anomaly Detection

Density Estimation

Problem Motivation7 min

Gaussian Distribution10 min

Algorithm12 min

Building an Anomaly Detection System

Developing and Evaluating an Anomaly Detection System13 min

Anomaly Detection vs. Supervised Learning7 min

Choosing What Features to Use12 min

Multivariate Gaussian Distribution (Optional)

Multivariate Gaussian Distribution13 min

Anomaly Detection using the Multivariate Gaussian Distribution14 min

Review

Lecture Slides 10 min

https://www.coursera.org/learn/machine-learning/supplement/pB0Jq/lecture-slides

Quiz: Anomaly Detection5 questions

Recommender Systems

Predicting Movie Ratings

Problem Formulation7 min

Content Based Recommendations14 min

Collaborative Filtering

Collaborative Filtering10 min

Collaborative Filtering Algorithm8 min

Low Rank Matrix Factorization

Vectorization: Low Rank Matrix Factorization8 min

Implementational Detail: Mean Normalization8 min

Review

Lecture Slides10 min

The course has ended. Assignments may not be resubmitted.

Quiz: Recommender Systems5 questions

Programming Assignment: Anomaly Detection and Recommender Systems 3h

Week 10

Large Scale Machine Learning

Gradient Descent with Large Datasets

Learning With Large Datasets5 min

Stochastic Gradient Descent13 min

Mini-Batch Gradient Descent6 min

Stochastic Gradient Descent Convergence11 min

Advanced Topics

Online Learning12 min

Map Reduce and Data Parallelism14 min

Review

Lecture Slides 10 min

Quiz: Large Scale Machine Learning5 questions

Week 11

Application Example: Photo OCR

Photo OCR

Problem Description and Pipeline7 min

Sliding Windows14 min

Getting Lots of Data and Artificial Data16 min

Ceiling Analysis: What Part of the Pipeline to Work on Next13 min

Review

Lecture Slides 10 min

https://www.coursera.org/learn/machine-learning/supplement/Q32e6/lecture-slides

The course has ended. Assignments may not be resubmitted.

Quiz: Application: Photo OCR5 questions

Conclusion

Summary and Thank You4 min