Lecture1 - Introduction.pdf
Lecture10 - Advice for Applying Machine Learning.pdf
Lecture11 - Machine Learning System Design.pdf
Lecture12 - Support Vector Machines.pdf
Lecture13 - Clustering.pdf
Lecture14 - Dimensionality Reduction.pdf
Lecture15 - Anomaly Detection.pdf
Lecture16 - Recommender Systems.pdf
Lecture17 - Large Scale Machine Learning.pdf
Lecture18 - application example photo OCR.pdf
Lecture2 - Model and Cost Function and Parameter Learning.pdf
Lecture3 - Linear Algebra Review.pdf
Lecture4 - Multivariate Linear regression.pdf
Lecture5 - Octave Tutorial.pdf
Lecture6 - Logistic Regression.pdf
Lecture7 - Regularization.pdf
Lecture8 - Neural Network Representation.pdf
Lecture9 - Neural Networks Learning.pdf
quiz week 1 - linear algebra.pdf
quiz week 1 - linear regression with one variable.pdf
quiz week 10 - large scale machine learning.pdf
quiz week 11 - application photo OCR.pdf
quiz week 2 - linear regression with multiple variables.pdf
quiz week 2 - octave matlab tutorial.pdf
quiz week 3 - logistic regression.pdf
quiz week 3 - regularization.pdf
quiz week 4 - neural networks representation.pdf
quiz week 5 - neural networks learning.pdf
quiz week 6 - advice for applying machine learning.pdf
quiz week 6 - machine learning system design.pdf
quiz week 7 - support vector machines.pdf
quiz week 8 - principal components analysis.pdf
quiz week 8 - unsupervised learning clustering.pdf
quiz week 9 - anomaly detection.pdf
quiz week 9 - recommender systems.pdf
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