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access_leaner-machine-learning-for-trading

implement and evaluate three learning algorithms as Python classes: A KNN learner, a Linear Regression learner (provided) and a Bootstrap Aggregating learner

access_portfolio

use pandas for reading in data, calculating various statistics and plotting a comparison graph.

backup

bidtree

rails app

edd_fes

form that need to be intgrated with

homework1-

nothing

htdocs

local mamp server to github

market_simulator

a market simulator that accepts trading orders and keeps track of a portfolio's value over time and then assesses the performance of that portfolio.

markov-decision-processess

explore Markov Decision Processes (MDPs):

optimize-portfolio-

find how much of a portfolio's funds should be allocated to each stock so as to optimize it's performance

pp

q_learning

In this project the purpose is to implement and assess Q-Learning. First it test Q-Learning implementation to solve a navigation problem. Then applying Q-Learning to stock trading. QLearner class has been implemented in the file QLearner.py.

randomized-optimization_ml

this project is to explore random search.

sphere_feature_login

stars_javescript-jquery

simple web with jQuery plugins

sub_project_fm

supervised-learning-_ml

explore some techniques in supervised learning

unsupervised-learning-and-dimensionality-reduction

explore unsupervised learning algorithms