About this course

 

 

 

 

 

 

What you'll learn

 

01: Introduction - Introduction to the course - What is machine learning? - Supervised learning - introduction - Unsupervised learning - introduction

 

02: Regression Analysis and Gradient Descent - Linear Regression - Linear regression - implementation (cost function) - A deeper insight into the cost function - simplified cost function - Gradient descent algorithm - So no need to change alpha over time - Linear regression with gradient descent

 

03: Linear Algebra -review - Matrices - overview - Vectors - overview - Matrix manipulation - Implementation/use - Matrix multiplication properties - Inverse and transpose operations

 

04: Linear Regression with Multiple Variables - Linear regression with multiple features - Gradient descent for multiple variables - Gradient Decent in practice: 1 Feature Scaling - Learning Rate a - Features and polynomial regression - Normal equation

 

05: Logistic Regression - Classification - Hypothesis representation - Decision boundary - Non-linear decision boundaries - Cost function for logistic regression - Simplified cost function and gradient descent - Advanced optimization - Multiclass classification problems

 

06: Regularization - The problem of overfitting - Cost function optimization for regularization - Regularized linear regression - Regularization with the normal equation - Advanced optimization of regularized linear regression

 

07: Neural Networks - Representation - Neural networks - Overview and summary - Model representation 1 - Model representation II - Neural network example - computing a complex, nonlinear function of the input - Multiclass classification

 

08: Neural Networks - Learning - Neural network cost functionx - Summary of what's about to go down - Backpropagation algorithm - Backpropagation intuition - Implementation notes - unrolling parameters (matrices) - Gradient checking - Random initialization - Putting it all together

 

09: Advice for applying machine learning techniques - Deciding what to try next - Evaluating a hypothesis - Model selection and training validation test sets - Diagnosis - bias vs. variance - Regularization and bias/variance - Learning curves

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10: Machine Learning System Design - Machine learning systems design - Prioritizing what to work on - spam classification example - Error metrics for skewed analysis - Trading off precision and recall - Data for machine learning

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11: Support Vector Machines - Support Vector Machine (SVM) - Optimization objective - Large margin intuition - Large margin classification mathematics (optional) - Kernels - 1: Adapting SVM to non-linear classifiers - Kernels II

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12: Clustering - Unsupervised learning - introduction - K-means algorithm - K means optimization objective - How do we choose the number of clusters?

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13: Dimensionality Reduction - Motivation 1: Data compression - Motivation 2: Visualization - Principle Component Analysis (PCA): Problem Formulation - PCA Algorithm - Reconstruction from Compressed Representation - Choosing the number of Principle Components - Advice for Applying PCA

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14: Anomaly Detection - Anomaly detection - problem motivation - The Gaussian distribution (optional) - Anomaly detection algorithm - Developing and evaluating and anomaly detection system - Anomaly detection vs. supervised learning - Choosing features to use - Multivariate Gaussian distribution - Applying multivariate Gaussian distribution to anomaly detection

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15: Recommender Systems - Recommender systems - introduction - Content based recommendation - Collaborative filtering - overview - Collaborative filtering Algorithm - Vectorization: Low rank matrix factorization - Implementation detail: Mean Normalization

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16: Large Scale Machine Learning - Learning with large datasets - Stochastic Gradient Descent - Mini Batch Gradient Descent - Stochastic gradient descent convergence - Online learning - Map reduce and data parallelism

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17: Application Example - Photo OCR - Problem description and pipeline - Sliding window image analysis

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18: Course Summary