Andrew Ng's Machine Learning Course

Here, we implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. In the next half, we go through some diagnostics of debugging learning algorithms and examine the effects of bias v.s. variance.

Machine learning with MATLAB/Octave, coding machine learning algorithms from scratch

📊 📈 In depth explained my assignment solutions. Grade: 97.3%

Machine Learning Algorithms for the programming tasks of Stanford online course from Andrew Ng on Coursera

Scripts for machine learning algorithms in MATLAB/Octave and python

Implementing regularised linear regression and using it to study models with different bias-variance properties. Insights for applying machine learning.

Machine Learning principles in Octave/Matlab from Andrew Ng Specialization

Bias/Variance dilemma, cross-validation and work on Iris Data Set from UCI Machine Learning Repository

Regularized linear regression model to predict the water flow from a dam | Examined effects of bias vs variance

Assignments of Machine Learning Course created by Stanford University taught by Andrew Ng

Machine Learning course that covers the most effective ML techniques, the theoretical underpinnings of learning, the practical knowledge needed to quickly and powerfully apply these techniques to new problems, and best practices in innovation as it pertains to machine learning and AI.

Bias and Variance Tradeoff for debugging

ML - Bias vs Variance - Python