Mathematics_for_Machine_Learning_and_Data_Science_Specialization

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Course content

1. Specialization & Course Introduction
- 1.Specialization introduction.mp4
- 2.Course introduction.mp4
- 3.What to expect and how to succeed.mp4
2. System of linear equations: 2 variables
- 1.Machine learning motivation.mp4
- 2.System of sentences.mp4
- 3.System of equations.mp4
- 4.System of equations as lines.mp4
- 5.A geometric notion of singularity.mp4
- 6.Singular vs nonsingular matrices.mp4
- 7.Linear dependence and independence.mp4
- 8.The determinant.mp4
Quiz
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3. System of linear equations: 3 variables
- 1.System of equations (3x3).mp4
- 2.Singular vs non-singular matrices (.mp4
- 3.System of equations as planes (3x3).mp4
- 4.Linear dependence and independence .mp4
- 5.The determinant (3x3).mp4
- 6.Conclusion.mp4
Quiz
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Notes
- P8uMS3oYSumhsZUJPLcJ-Q_8032d8f134064017b63bd7d7ed1fddf1_C1_W1.pdf
ungraded lab 1
- C1_W1_Lab_1_introductio.ipynb
- Files.zip
ungraded lab 2
- C1_W1_Lab_2_solving_linear_systems_2_variables.ipynb
- Files.zip
1. Solving system of linear equations: Elimination
- 1.Machine learning motivation.mp4
- 2.Solving non-singular system of linear equations.mp4
- 3.Solving singular systems of linear equations.mp4
- 4.Solving systems of equations with more variables.mp4
- 5.Matrix row-reduction.mp4
- 6.Row operations that preserve singularity.mp4
Quiz
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2.Solving system of linear equation: Row echelon form and rank
- 1.The rank of a matrix.mp4
- 2.The rank of a matrix in general.mp4
- 3.Row echelon form.mp4
- 4.Row echelon form in general.mp4
- 5.Reduced row echelon form.mp4
- 6.Conclusion.mp4
Lab
- C1_W2_Assig.ipynb
Notes
- ZKsoyNprQ8uqLjXBzyTFtg_e6825bb328344571a932637ad81fdbf1_C1_W2.pdf
Quiz
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Lab
- C1_W2_Lab_1_solving_lin.ipynb
- Files.zip
Programming Assignment: System of Linear Equations
- C1_W2_Assignment.ipynb
- Files.zip
1. Vector algebra
- 1.Machine Learning Motivation.mp4
- 2.Vectors and their properties.mp4
- 3.Sum and difference of vectors.mp4
- 4.Distance between vectors.mp4
- 5.Multiplying a vector by a scalar.mp4
- 6.The dot product.mp4
- 7.Geometric Dot Product.mp4
- 8.Multiplying a matrix by a vector.mp4
Lab
- C1_W3_Lab_1_vector_operations.ipynb
- Files.zip
Quiz
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2. Linear transformations
- 1.Matrices as linear transformations.mp4
- 2.Linear transformations as matrices.mp4
- 3.Matrix multiplication.mp4
- 4.The identity matrix.mp4
- 5.Matrix inverse.mp4
- 6.Which matrices have an inverse?.mp4
- 7.Neural networks and matrices.mp4
- 8.Conclusion.mp4
Quiz
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Assignment
- C1_W3_Assignment.ipynb
- File.zip
Lab
- C1_W3_Lab_2_matrix_multiplication.ipynb
- C1_W3_Lab_3_linear_transformations.ipynb
Linear Transformations
- Files.zip
Matrix Multiplication
- Files.zip
Notes
- hZPic6iIQ4STLCToYlV8aQ_2c37fce670c24944bc815eccc34a61f1_C1_W3.pdf
1. Determinants In-depth
- 1.Machine learning motivation.mp4
- 2.Singularity and rank of linear transformations.mp4
- 3.Determinant as an area.mp4
- 4.Determinant of a product.mp4
- 5.Determinants of inverses.mp4
Quiz
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2. Eigenvalues and Eigenvectors
- 1.Bases in Linear Algebra.mp4
- 2.Span in Linear Algebra.mp4
- 3.Eigenbases.mp4
- 4.Eigenvalues and eigenvectors.mp4
- 5.Conclusion.mp4
Quiz
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Assignment
- C1_W4_Assignment.ipynb
- File.zip
Notes
- M9kF_S_TQP6tVpKxeitgQA_61f355b33f2c40058420387626b094f1_C1_W4.pdf
1. Lesson 1 - Derivatives
- 1.Course Introduction by Andrew Ng.mp4
- 2.Course Introduction by Luis Serrano.mp4
- 3.Machine Learning Motivation.mp4
- 4.Motivation to Derivatives - Part I.mp4
- 5.Derivatives and Tangents.mp4
- 6.Slopes, maxima and minima.mp4
- 7.Derivatives and their notation.mp4
- 8.Some common derivatives - Lines.mp4
- 9.Some common Derivatives - Quadratics.mp4
- 10.Some common derivatives - Higher degree polynomials.mp4
- 11.Some common derivatives - Other power functions.mp4
- 12.The inverse function and its derivative.mp4
- 13.Derivative of trigonometric functions.mp4
- 14.Meaning of the Exponential (e).mp4
- 15.The derivative of e^x.mp4
- 16.The derivative of log(x).mp4
- 17.Existence of the derivative.mp4
- 18.Properties of the derivative: Multiplication by scalars.mp4
- 19.Properties of the derivative: The sum rule.mp4
- 20.Properties of the derivative: The product rule.mp4
- 21.Properties of the derivative: The chain rule.mp4
Quiz
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2. Lesson 2 - Optimization
- 1.Introduction to Optimization.mp4
- 2.Optimization of squared loss - The one powerline problem.mp4
- 3.Optimization of squared loss - The two powerline problem.mp4
- 4.Optimization of squared loss - The three powerline problem.mp4
- 5.Optimization of log-loss - Part 1.mp4
- 6.Optimization of log-loss - Part 2.mp4
- 7.Week 1 - Conclusion.mp4
Quiz
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Assignment
- C2_W1_Assignment.ipynb
- Files.zip
Lab
- C2_W1_Lab_1_differentiation_in_python.ipynb
- Files.zip
Notes
- Wj84GnP7T6G7b5lpVrtxCA_20f6b53e63524b9e83d239b23c1a62f1_C2_W1.pdf
1. Lesson 1 - Gradients
- 1.Introduction to Tangent planes.mp4
- 2.Partial derivatives - Part 1.mp4
- 3.Partial derivatives - Part 2.mp4
- 4.Gradients.mp4
- 5. Gradients and maximaminima.mp4
- 6.Optimization with gradients: An example.mp4
- 7.Optimization using gradients - Analytical method.mp4
Quiz
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2. Lesson 2 - Gradient Descent
- 1.Optimization using Gradient Descent in one variable - Part 1.mp4
- 2.Optimization using Gradient Descent in one variable - Part 2.mp4
- 3.Optimization using Gradient Descent in one variable - Part 3.mp4
- 4.Optimization using Gradient Descent in two variables - Part 1.mp4
- 5.Optimization using Gradient Descent in two variables - Part 2.mp4
- 6.Optimization using Gradient Descent - Least squares.mp4
- 7.Optimization using Gradient Descent - Least squares with multiple observations.mp4
- 8.Week 2 - Conclusion.mp4
Assignment
- C2_W2_Assignment.ipynb
- Files.zip
Lab
- C2_W2_Lab_1_Optimization_Using_Gradient_Descent_in.ipynb
- C2_W2_Lab_2_Optimization_Using_Gradient_Descent_in.ipynb
Optimization Using Gradient Descent in One Variable
- Files.zip
Optimization Using Gradient Descent in Two Variables
- Files.zip
Quiz
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Notes
- BeZewfdIQ5OXn36eGUvChQ_a998f9d0784541e09f89b98b1f77d3f1_C2_W2.pdf
1. Lesson 1 - Optimization in Neural Networks
- 1.Regression with a perceptron.mp4
- 2.Regression with a perceptron - Loss function.mp4
- 3.Regression with a perceptron - Gradient Descent.mp4
- 4.Classification with Perceptron.mp4
- 5.Classification with Perceptron - The sigmoid function.mp4
- 6.Classification with Perceptron - Gradient Descent.mp4
- 7.Classification with Perceptron - Calculating the derivatives.mp4
- 8.Classification with a Neural Network.mp4
- 9.Classification with a Neural Network - Minimizing log-loss.mp4
- 10.Gradient Descent and Backpropagation.mp4
Lab
- C2_W3_Lab_1_Regression_with_Percept.ipynb
- C2_W3_Lab_2_Classification_with_Per.ipynb
Classification with Perceptron
- Files.zip
Regression with Perceptron
- Files.zip
Quiz
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2. Lesson 2 - Newton is Method
- 1.Newton is Method.mp4
- 2.Newton is Method: An example.mp4
- 3.The second derivative.mp4
- 4.The Hessian.mp4
- 5.Hessians and concavity.mp4
- 6.Newton is Method for two variables.mp4
- 7.Week 3 - Conclusion.mp4
Assignment
- C2_W3_Assignment.ipynb
- Files.zip
Lab
- C2_W3_Lab_3_Optimization_Using_Newtons_Method.ipynb
- Files.zip
Quiz
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Notes
- dt8Laum9SV-8LgXXdddwIA_8ad8b7c7c8284a32844cde851a28f2f1_C2_W3.pdf

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