An a bias-variance tradeoff of Sarsa vs. Expected Sarsa with experiments.

Predicting Boston Housing Prices

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

Contains assignments I did in ML course

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

Machine Learning principles in Octave/Matlab from Andrew Ng Specialization

Machine Learning exercises in Python (Jupyter notebooks)

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

Brief study on Underfitting and Overfitting in Machine Learning

3D property modeling using geostatistics

Andrew Ng's Machine Learning Course

Applied basic machine learning concepts on data collected for housing prices in the Boston, Massachusetts area to predict the selling price of a new home

Builded a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

A visual example of the concepts of under and overfitting in supervised machine learning using U.S. state border data.

Bias variance experiment from Learning from Data. Problem 2.24, p. 75.

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

Scripts for machine learning algorithms in MATLAB/Octave and python

A five-course specialization covering the foundations of Deep Learning, from building CNNs, RNNs & LSTMs to choosing model configurations & paramaters like Adam, Dropout, BatchNorm, Xavier/He initialization, and others.

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.