Machine Learning - Stanford University.

Taught by Prof. Andrew Ng.

Verified Certificate

[Slides 1-3] [Lecture Notes]

• Supervised/Unsupervised Learning
• Linear Regression with One Variable
• Gradient Descent for Linear Regression
• Linear Algebra Review

[Slides 4-5] [Lecture Notes] [Assignment]

• Multivariate Linear Regression
• Normal Equation
• Octave/Matlab basic tutorial

[Slides 6-7] [Lecture Notes] [Assignment]

• Classification
• Logistic Regression Model
• Multiclass Classification
• Overfitting
• Regularization

[Slides 8] [Lecture Notes] [Assignment]

• Model Representation
• Multiclass Classification

[Slides 9] [Lecture Notes] [Assignment]

• Foward Propagation
• Backward Propagation
• Gradient Checking
• Random Initalization

[Slides 10-11] [Lecture Notes] [Assignment]

• Evaluating a hypothesis
• Bias vs Variance
• Regularization
• Error Analysis
• Handling Skewed Data
• Spam Classifier

[Slides 12] [Lecture Notes] [Assignment]

• Large Margin Classification with SVM
• Kernels

[Slides 13-14] [Lecture Notes] [Assignment]

• Clustering: K-means Algorithms
• Dimensionality Reduction: Principal Component Analysis (PCA)

[Slides 15-16] [Lecture Notes] [Assignment]

• Density Estimation
• Anomaly Detection
• Recommender System

[Slides 17] [Lecture Notes]

• Stochastic Gradient Descent
• Mini-batch Gradient Descent
• Online Learning

[Slides 18]

• Sliding Window
• Machine Learning pipeline