Machine Learning ~ Stanford 📖 Overview Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Certificate Week 1 Quizzes Introduction Linear Regression With One Variable Linear Algebra Week 2 Quizzes Linear Regression With Multiple Variables Octave/Matlab Tutorial Programming Exercises Questions and Explanation Exercise 1 Warm Up Exercise Compute Cost for One Variable Compute Cost For Multiple Variables Gradient Descent For One Variable Gradient Descent For Multiple Variables Week 3 Quizzes Logistic Regression Regularization Programming Exercises Questions Exercise 2 Sigmoid Function Logistic Regression Cost Logistic Regression Gradient Regularized Logistic Regression Cost Regularized Logistic Regression Gradient Predict Week 4 Quizzes Neural Networks: Representation Programming Exercises Questions and Explanations Exercise 3 Logistic Regression Cost Function One vs. All Multi Class Classifier Predict one vs. all Multi Class Classifier Neural Network Prediction Function Week 5 Quizzes Neural Networks: Learning Programming Exercises Questions and Explanations Exercise 4 Feedforward and Cost Function Regularized Cost Function Compute Gradient of Sigmoid Function Randomly Initialize Weights Neural Network Gradient (Backpropagation) Neural Network Cost Function Regularized Gradient Week 6 Quizzes Advice for Applied Machine Learning Machine Learning System Design Quiz Programming Exercises Questions and Explanations Exercise 5 Regularized Linear Regression Cost Function Generate Learning Curve Maps Data into Polynomial Feature Space Generates a Cross Validation Curve Week 7 Quizzes Support Vector Machines (SVM) Programming Exercises Questions and Explanations Exercise 6: Support Vector Machines Gaussian Kernel for SVM Parameters to Use for Dataset 3 Email Preprocessing Feature Extraction From Email Week 8 Quizzes Unsupervised Learning Principal Component Analysis Programming Exercises Exercise 7: Questions and Explanations K Means Clustering and PCA(Principal Component Analysis) Perform PCA(Principal Component Analysis) Project a Dataset into lower dimensional space Recover the Original Data from the Projection Find Closest Centroids Using K-Means Compute Centroid Means Initialize K means for Centroids Week 9 Quizzes Anomaly Detection Reccomender Systems Programming Exercises Exercise 8: Questions and Explanations Anomaly Detection and Reccomender Systems Estimate Gaussian Parameters Find Threshold for Anomaly Detection Cost Function for Collaborative Filterinf Week 10 Quizzes Large Scale Machine Learning Week 11 Quizzes Application: Photo OCR 🎓 Certificate
