bias-variance

Notes for Deep Learning Specialization Courses led by Andrew Ng.

This repository is a recommended track, designed to get started with Machine Learning.

A document covering machine learning basics. 🤖📊

[WIP] In this repository I implemented all assignments in python. (No octave to python library)

Andrew Ng's Machine Learning Course

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

Chapter 2: Machine Learning Basics

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

Machine Learning exercises in Python (Jupyter notebooks)

This is a simple python example to demonstrate bias variance

Notes on Machine Learning with DataSets and Examples

Machine Learning models on Anomaly detection, Recommender system on movies based on IMDB dataset, Digit Identification using Logistic regression, Neural network based facial feature recognition, PCA, SVM based Spam filter, Logistic Regression - Nelder Mead

Explanation of the Bias Variance Tradeoff in Machine Learning

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.

Practice from My Machine Learning Certificate from Cornell

Machine Learning principles in Octave/Matlab from Andrew Ng Specialization

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

A Mathematical Intuition behind Linear Regression Algorithm

3D property modeling using geostatistics

Academic projects carried out as part of the Introduction to Machine Learning course.

Scripts for machine learning algorithms in MATLAB/Octave and python

This project investigates if and how systematic subsampling can be applied to imbalanced learning.

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

Collection of my notes from Udacity's Intro to Deep Learning--> Introduction to Neural Networks 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.

Trabalhos e notas de aula da disciplina MAC0460 - Introdução ao Aprendizado de Máquina, no primeiro semestre de 2021, no IME-USP.

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

Comparing Three Penalized Least Squares Estimators: LASSO,SCAD and MCP.

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.

Create a Deep Neural Network from Scratch using Python3.