Machine-Learning from Coursera as offered by Andrew Ng from Stanford
Statement of Accomplishment: https://github.com/fissehab/fissehab.github.io/blob/master/figures/mlandrew.pdf (Grade Achieved: 100.0%)
• Introduction
• Linear Regression with One Variable
• Linear Algebra Review
• Review Questions (for the week's topics)
• Linear Regression with Multiple Variables
• Octave Tutorial
• Review Questions (for the week's topics)
• Programming Exercise 1 (Linear regression)
• Logistic Regression
• Regularization
• Review Questions (for the week's topics)
• Programming Exercise 2 (Logistic regression)
• Neural Networks: Representation
• Review Questions (for the week's topics)
• Programming Exercise 3 (Multi-class classification and neural networks)
• Neural Networks: Learning
• Review Questions (for the week's topics)
• Programming Exercise (Neural network learning)
• Advice for Applying Machine Learning
• Machine Learning System Design
• Review Questions (for the week's topics)
• Programming Exercise (Bias-variance)
• Support Vector Machines (SVMs)
• Review Questions (for the week's topics)
• Programming Exercise (SVMs)
• Clustering
• Dimensionality Reduction
• Review Questions (for the week's topics)
• Programming Exercise (K-Means and PCA)
• Anomaly Detection
• Recommender Systems
• Review Questions (for the week's topics)
• Programming Exercise (Anomaly Detection and Recommender Systems)
• Large-Scale Machine Learning
• Example of an application of machine learning
• Review Questions (for the week's topics)