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[2019] MACHINE LEARNING REGRESSION MASTERCLASS IN PYTHON

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Link: [2019] MACHINE LEARNING REGRESSION MASTERCLASS IN PYTHON In Courses Buddy you will find the best online courses on the categories you want to learn. Our team explores many courses in many ...BESTSELLER 4.7 (41 ratings) 727 students enrolled Created by Dr. Ryan Ahmed, Ph.D., MBA, Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, Mitchell Bouchard What you'll learn Master Python programming and Scikit learn as applied to machine learning regression Understand the underlying theory behind simple and multiple linear regression techniques Apply simple linear regression techniques to predict product sales volume and vehicle fuel economy Apply multiple linear regression to predict stock prices and Universities acceptance rate Cover the basics and underlying theory of polynomial regression Apply polynomial regression to predict employees' salary and commodity prices Understand the theory behind logistic regression Apply logistic regression to predict the probability that customer will purchase a product on Amazon using customer features Understand the underlying theory and mathematics behind Artificial Neural Networks Learn how to train network weights and biases and select the proper transfer functions Train Artificial Neural Networks (ANNs) using back propagation and gradient descent methods Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance Apply ANNs to predict house prices given parameters such as area, number of rooms..etc Assess the performance of trained Machine learning models using KPI (Key Performance indicators) such as Mean Absolute error, Mean squared Error, and Root Mean Squared Error intuition, R-Squared intuition, Adjusted R-Squared and F-Test Understand the underlying theory and intuition behind Lasso and Ridge regression techniques Sample real-world, practical projects Requirements Machine Learning basics PC with Internet connetion Artificial Intelligence (AI) revolution is here! The technology is progressing at a massive scale and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. Machine Learning is a subfield of Artificial Intelligence that enables machines to improve at a given task with experience. Machine Learning is an extremely hot topic; the demand for experienced machine learning engineers and data scientists has been steadily growing in the past 5 years.


How to prepare a dataset? – MyDataModels

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Artificial intelligence and machine learning became, in a few years, key technologies for professionals and organizations to master, to stay in the game and ahead of the competition. Organizations are starting to invest heavily in machine learning, and we already see highly positive results. In simple words, a dataset is a collection of data. It is usually organized as a table with data and column names. Not very different than what you are used to work with when using Excel.


Stock Forecasting using M-Band Wavelet-Based SVR and RNN-LSTMs Models

arXiv.org Machine Learning

The task of predicting future stock values has always been one that is heavily desired albeit very difficult. This difficulty arises from stocks with non-stationary behavior, and without any explicit form. Hence, predictions are best made through analysis of financial stock data. To handle big data sets, current convention involves the use of the Moving Average. However, by utilizing the Wavelet Transform in place of the Moving Average to denoise stock signals, financial data can be smoothened and more accurately broken down. This newly transformed, denoised, and more stable stock data can be followed up by non-parametric statistical methods, such as Support Vector Regression (SVR) and Recurrent Neural Network (RNN) based Long Short-Term Memory (LSTM) networks to predict future stock prices. Through the implementation of these methods, one is left with a more accurate stock forecast, and in turn, increased profits.


Zhenye-Na/DA-RNN

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This dataset is a subset of the full NASDAQ 100 stock dataset used in [1]. It includes 105 days' stock data starting from July 26, 2016 to December 22, 2016. Each day contains 390 data points except for 210 data points on November 25 and 180 data points on Decmber 22. Some of the corporations under NASDAQ 100 are not included in this dataset because they have too much missing data. There are in total 81 major coporations in this dataset and we interpolate the missing data with linear interpolation.


Universal features of price formation in financial markets: perspectives from Deep Learning

arXiv.org Machine Learning

Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of electronic market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary price formation mechanism relating the dynamics of supply and demand for a stock, as revealed through the order book, to subsequent variations in its market price. We assess the model by testing its out-of-sample predictions for the direction of price moves given the history of price and order flow, across a wide range of stocks and time periods. The universal price formation model is shown to exhibit a remarkably stable out-of-sample prediction accuracy across time, for a wide range of stocks from different sectors. Interestingly, these results also hold for stocks which are not part of the training sample, showing that the relations captured by the model are universal and not asset-specific. The universal model --- trained on data from all stocks --- outperforms, in terms of out-of-sample prediction accuracy, asset-specific linear and nonlinear models trained on time series of any given stock, showing that the universal nature of price formation weighs in favour of pooling together financial data from various stocks, rather than designing asset- or sector-specific models as commonly done. Standard data normalizations based on volatility, price level or average spread, or partitioning the training data into sectors or categories such as large/small tick stocks, do not improve training results. On the other hand, inclusion of price and order flow history over many past observations is shown to improve forecasting performance, showing evidence of path-dependence in price dynamics.