Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language

🎓 My lecture notes and assignment solutions for the Coursera machine learning class taught by Andrew Ng.

Machine Learning Exercises from Online Course (Coursera)

Tuner Implementation of Extrapolating Learning Curves

Machine learning techniques, such as Linear Regression, Logistic Regression, Neural Networks (feedforward propagation, backpropagation algorithms), Diagnosing Bias/Variance, Evaluating a Hypothesis, Learning Curves, Error Analysis, Support Vector Machines, K-Means Clustering, PCA, Anomaly Detection System, and Recommender System.

Exploring deep learning on Cooley

Bias v.s. Variance

Some applications on functions of sklearn.learning_curve

Predicting Boston Housing Prices

Supervised regression to predict Boston housing price

Mini sorter for country organizations based on jQuery.

Developed simple web-application for testing (DOM) functionalities based on Bootstrap

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

Developed user interface with own preferable customization

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

Built 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 Comprehensive Guide to Titanic Machine Learning from Disaster

Developing very simple and default user interface

Developed number guessing game base on Plain JavaScript

Use the DecisionTreeRegressor for predicting the house price based on Boston housing dataset.

Default and basic console applications, which use for learning necessary themes of JavaScript

Machine Learning Nano-degree Project : To assist a real estate agent and his/her client with finding the best selling price for their home