Taking the Coursera Machine Learning course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng.
Assumes you have knowledge of Week 5.
Table of Contents Advice for Applying Machine Learning Evaluating a Learning Algorithm Evaluating a Hypothesis Model Selection and Train/Validation/Test Sets Diagnosing Bias versus Variance Regularization and Bias/Variance Learning Curves Deciding What to Do Next Lecture notes: Lecture10 Advice for Applying Machine Learning Evaluating a Learning Algorithm Evaluating a Hypothesis Once we have done some trouble shooting for errors in our predictions by:

Taking the Coursera Machine Learning course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng.
Assumes you have knowledge of Week 4.
Table of Contents Neural Networks: Learning Cost Function and Backpropagation Cost Function Backpropagation Algorithm Backpropagation Intuition Backpropagation in Practice Implementation Note: Unrolling Parameters Gradient Checking Random Initialization Putting it Together Application of Neural Networks Autonomous Driving Lecture notes: Lecture9 Neural Networks: Learning Cost Function and Backpropagation Cost Function Let’s define a few variables that we will need to use.

Taking the Coursera Machine Learning course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng.
Assumes you have knowledge of Week 3.
Table of Contents Neural Networks: Representation Motivations Non-linear Hypothesis Neurons and the Brain Neural Networks Model Representation I Model Representation II Applications Examples and Intuitions I Examples and Intuitions II Multiclass Classification Lecture notes: Lecture8 Neural Networks: Representation Motivations Non-linear Hypothesis Neural networks are another learning algorithm that exist in addition to linear regression and logistic regression.

Taking the Coursera Machine Learning course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng.
Assumes you have knowledge of Week 2.
Table of Contents Logistic Regression Classification and Representation Classification Hypothesis Representation Decision Boundary Logistic Regression Model Cost Function Simplified Cost Function and Gradient Descent Advanced Optimization Multiclass Classification Multiclass Classification: One-vs-all Regularization Solving the Problem of Overfitting The Problem of Overfitting Cost Function Regularized Linear Regression Regularized Logistic Regression Lecture notes: Lecture6 Lecture7 Logistic Regression Classification and Representation Classification Recall that classification involves a hypothesis function which returns a discontinuous output (common example was whether or not a tumor was benign or cancerous based on size).

Taking the Coursera Machine Learning course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng.
Assumes you have knowledge of Week 1.
Table of Contents Linear Regression with Multiple Variables Multivariate Linear Regression Multiple Features Gradient Descent for Multiple Variables Gradient Descent in Practice - Feature Scaling & Mean Normalization Gradient Descent in Practice - Learning Rate Features and Polynomial Regression Computing Parameters Analytically Normal Equation Normal Equation Noninvertibility Optional Octave/MatLab Tutorial Octave Tutorial Basic Operations Moving Data Around Computing on Data Plotting Data Functions & Control Statements: for, while, if/elseif/else Vectorization Lecture notes: Lecture4 Lecture5 Linear Regression with Multiple Variables Multivariate Linear Regression Multiple Features Linear regression with multiple variables is known as Multivariate Linear Regression.

Taking the Coursera Machine Learning course. Will post condensed notes every week as part of the review process. All material originates from the free Coursera course, taught by Andrew Ng.
Table of Contents Introduction Machine Learning What is Machine Learning Supervised Learning Unsupervised Learning Linear Regression with One Variable Model Representation Cost Function & Intuitions Gradient Descent Gradient Descent for Linear Regression Optional Linear Algebra Linear Algebra Review Matrices and Vectors Matrix Addition and Scalar Operations Matrix-Vector Multiplication Matrix-Matrix Multiplication Matrix Multiplication Properties Inverse and Transpose Lecture notes: Lecture1 Lecture2 Lecture3 Introduction Machine Learning What is Machine Learning Arthur Samuel (1959): The field of study that gives computers the ability to learn without explicitly programmed.

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