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LICENSE (1072, 2021-11-09)
Lecture Notes/ (0, 2021-11-09)
Lecture Notes/Lecture_10_Advice_For_Applying_Machine_Learning.pdf (1554620, 2021-11-09)
Lecture Notes/Lecture_11_Machine_Learning_System_Design.pdf (509584, 2021-11-09)
Lecture Notes/Lecture_12_Support_Vector_Machine_ALgorithm.pdf (2415325, 2021-11-09)
Lecture Notes/Lecture_13_Clustering_UnsuperVised_Learning_K_MEans.pdf (2280366, 2021-11-09)
Lecture Notes/Lecture_14_Dimensionality_Reduction_PCA.pdf (1685108, 2021-11-09)
Lecture Notes/Lecture_15_Anomaly _Detection.pdf (3488418, 2021-11-09)
Lecture Notes/Lecture_16_Recommender_Systems_Collaborative_Learning_Algorithm.pdf (1485594, 2021-11-09)
Lecture Notes/Lecture_17_Large_Scale_machine_Learning_Large_Data_Sets.pdf (2075061, 2021-11-09)
Lecture Notes/Lecture_18_Application_Example_Photo_OCR_Pipeline.pdf (2067134, 2021-11-09)
Lecture Notes/Lecture_1_Introduction.pdf (3461775, 2021-11-09)
Lecture Notes/Lecture_2_Linear_Regression_Cost_Function_Gradient_Descent.pdf (3018645, 2021-11-09)
Lecture Notes/Lecture_3_Linear_Algebra_Matrices.pdf (1888369, 2021-11-09)
Lecture Notes/Lecture_4_Multivariate_Linear_Regression_Normal_Equation.pdf (1783318, 2021-11-09)
Lecture Notes/Lecture_5_Octave_Tutorial.pdf (248191, 2021-11-09)
Lecture Notes/Lecture_6_Classification_Logistic_Regression.pdf (2221463, 2021-11-09)
Lecture Notes/Lecture_7_Regularization.pdf (3383569, 2021-11-09)
Lecture Notes/Lecture_8_Neural_Networks_Representation.pdf (5215888, 2021-11-09)
Lecture Notes/Lecture_9_Neural_Networks_Learning.pdf (3554480, 2021-11-09)
Programming Exercise 1/ (0, 2021-11-09)
Programming Exercise 1/computeCost.asv (667, 2021-11-09)
Programming Exercise 1/computeCost.m (667, 2021-11-09)
Programming Exercise 1/computeCostMulti.m (671, 2021-11-09)
Programming Exercise 1/ex1.mlx (323244, 2021-11-09)
Programming Exercise 1/ex1.pdf (489928, 2021-11-09)
Programming Exercise 1/ex1_companion.mlx (9526, 2021-11-09)
Programming Exercise 1/featureNormalize.m (1249, 2021-11-09)
Programming Exercise 1/gradientDescent.asv (966, 2021-11-09)
Programming Exercise 1/gradientDescentMulti.m (933, 2021-11-09)
Programming Exercise 1/lib/ (0, 2021-11-09)
Programming Exercise 1/lib/jsonlab/ (0, 2021-11-09)
Programming Exercise 1/lib/jsonlab/ChangeLog.txt (3862, 2021-11-09)
Programming Exercise 1/lib/jsonlab/LICENSE_BSD.txt (1551, 2021-11-09)
Programming Exercise 1/lib/jsonlab/jsonopt.m (881, 2021-11-09)
Programming Exercise 1/lib/jsonlab/loadjson.m (18732, 2021-11-09)
Programming Exercise 1/lib/jsonlab/mergestruct.m (771, 2021-11-09)
Programming Exercise 1/lib/jsonlab/saveubjson.m (16123, 2021-11-09)
Programming Exercise 1/lib/jsonlab/varargin2struct.m (1094, 2021-11-09)
Programming Exercise 1/lib/makeValidFieldName.m (1195, 2021-11-09)
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# Machine-Learning Created by Standford University. Instructor: [Andrew Ng](http://www.andrewng.org/) ## Contents * Lectures Slides * My Solutions to Programming Assignments Implemented in MATLAB ## Introduction This course explicitly explained the basics of machine learning, data mining, and statistical pattern recognition. Topics include: 1. **Supervised Learning:** Linear Regression, Logistic Regression, Support Vector Machines (SVM), Kernels, Neural networks. 2. **Unsupervised Learning:** Clustering, Dimensionality Reduction, Anomaly Detection, Recommender systems 3. **Best Practices in Machine Learning:** Bias/Variance theory; Innovation Process in Machine Learning and AI. ## References [[1] Machine Learning - Stanford University](https://www.coursera.org/learn/machine-learning) ## Certification 

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