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Math for Machine Learning

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01 - Course Promo.mp4
01 - Course Promo.mp4
02 - Course Introduction.mp4
02 - Course Introduction.mp4
03 - Linear Regression.mp4
03 - Linear Regression.mp4
04 - The Least Squares Method.mp4
04 - The Least Squares Method.mp4
05 - Linear Algebra Solution to Least Squares Problem.mp4
05 - Linear Algebra Solution to Least Squares Problem.mp4
06 - Example Linear Regression.mp4
06 - Example Linear Regression.mp4
07 - Summary Linear Regression.mp4
07 - Summary Linear Regression.mp4
08 - Classification.mp4
08 - Classification.mp4
09 - Linear Discriminant Analysis.mp4
09 - Linear Discriminant Analysis.mp4
10 - The Posterior Probability Functions.mp4
10 - The Posterior Probability Functions.mp4
11 - Modelling the Posterior Probability Functions.mp4
11 - Modelling the Posterior Probability Functions.mp4
12 - Linear Discriminant Functions.mp4
12 - Linear Discriminant Functions.mp4
13 - Estimating the Linear Discriminant Functions.mp4
13 - Estimating the Linear Discriminant Functions.mp4
14 - Classifying Data Points Using Linear Discriminant Functions.mp4
14 - Classifying Data Points Using Linear Discriminant Functions.mp4
15 - LDA Example 1.mp4
15 - LDA Example 1.mp4
16 - LDA Example 2.mp4
16 - LDA Example 2.mp4
17 - Summary Linear Discriminant Analysis.mp4
17 - Summary Linear Discriminant Analysis.mp4
18 - Logistic Regression.mp4
18 - Logistic Regression.mp4
19 - Logistic Regression Model of the Posterior Probability Function.mp4
19 - Logistic Regression Model of the Posterior Probability Function.mp4
20 - Estimating the Posterior Probability Function.mp4
20 - Estimating the Posterior Probability Function.mp4
21 - The Multivariate Newton-Raphson Method.mp4
21 - The Multivariate Newton-Raphson Method.mp4
22 - Maximizing the Log-Likelihood Function.mp4
22 - Maximizing the Log-Likelihood Function.mp4
23 - Logistic Regression Example.mp4
23 - Logistic Regression Example.mp4
24 - Summary Logistic Regression.mp4
24 - Summary Logistic Regression.mp4
25 - Artificial Neural Networks.mp4
25 - Artificial Neural Networks.mp4
26 - Neural Network Model of the Output Functions.mp4
26 - Neural Network Model of the Output Functions.mp4
27 - Forward Propagation.mp4
27 - Forward Propagation.mp4
28 - Choosing Activation Functions.mp4
28 - Choosing Activation Functions.mp4
29 - Estimating the Output Functions.mp4
29 - Estimating the Output Functions.mp4
30 - Error Function for Regression.mp4
30 - Error Function for Regression.mp4

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