Project files from course: https://www.coursera.org/learn/machine-learning
###About this Course
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Subtitles available in English, Portuguese, Spanish, Japanese, Chinese (Simplified) Instructors
Instructor photo Andrew Ng Associate Professor, Stanford University; Chief Scientist, Baidu; Chairman and Co-founder, Coursera Syllabus
###Week 1
- Introduction
- Linear Regression with One Variable
- Linear Algebra Review
- Environment Setup Instructions
- Introduction
- Review
- Course Wiki Lecture Notes
- Model and Cost Function
- Parameter Learning
- Review
- Linear Algebra Review
- Review
- Quiz: Introduction
- Quiz: Linear Regression with One Variable
###Week 2
- Linear Regression with Multiple Variables
- Octave Tutorial
- Multivariate Linear Regression
- Computing Parameters Analytically
- Review
- Octave Tutorial
- Submitting Programming Assignments
- Review
- Quiz: Linear Regression with Multiple Variables
- Assignment: Linear Regression
- Quiz: Octave Tutorial
###Week 3
- Logistic Regression
- Regularization
- Classification and Representation
- Logistic Regression Model
- Multiclass Classification
- Review
- Solving the Problem of Overfitting
- Review
- Quiz: Logistic Regression
- Assignment: Logistic Regression
- Quiz: Regularization
###Week 4
- Neural Networks: Representation
- Motivations
- Neural Networks
- Applications
- Review
- Quiz: Neural Networks: Representation
- Assignment: Multi-class Classification and Neural Networks
###Week 5
- Neural Networks: Learning
- Cost Function and Backpropagation
- Backpropagation in Practice
- Application of Neural Networks
- Review
- Quiz: Neural Networks: Learning
- Assignment: Neural Network Learning
###Week 6
- Advice for Applying Machine Learning
- Machine Learning System Design
- Evaluating a Learning Algorithm
- Bias vs. Variance
- Review
- Building a Spam Classifier
- Handling Skewed Data
- Using Large Data Sets
- Review
- Quiz: Advice for Applying Machine Learning
- Assignment: Regularized Linear Regression and Bias/Variance
- Quiz: Machine Learning System Design
###Week 7
- Support Vector Machines
- Large Margin Classification
- Kernels
- SVMs in Practice
- Review
- Quiz: Support Vector Machines
- Assignment: Support Vector Machines
###Week 8
- Unsupervised Learning
- Dimensionality Reduction
- Clustering
- Review
- Motivation
- Principal Component Analysis
- Applying PCA
- Review
- Quiz: Unsupervised Learning
- Quiz: Principal Component Analysis
- Assignment: K-Means Clustering and PCA
###Week 9
- Anomaly Detection
- Recommender Systems
- Density Estimation
- Building an Anomaly Detection System
- Multivariate Gaussian Distribution (Optional)
- Review
- Predicting Movie Ratings
- Collaborative Filtering
- Low Rank Matrix Factorization
- Review
- Quiz: Anomaly Detection
- Quiz: Recommender Systems
- Assignment: Anomaly Detection and Recommender Systems
###Week 10
- Large Scale Machine Learning
- Gradient Descent with Large Datasets
- Advanced Topics
- Review
- Quiz: Large Scale Machine Learning
###Week 11
- Application Example: Photo OCR
- Photo OCR
- Review
- Conclusion
- Quiz: Application: Photo OCR