KNIME is an open source data analytics platform for data science, ML, AI, AutoML, big data, & more. This is a course for Business Enthusiasts who look for data-driven decision-making techniques for different business scenarios. This would provide a basic and intermediate level understanding of different Machine Learning Algorithms and how they can be implemented in KNIME. It would also teach the students how to judge the different Machine Learning Algorithms and which ones will fit your business scenario. KNIME is free and powerful software that has a vast number of business use cases.

KNIME is an open source data analytics platform for data science, ML, AI, AutoML, big data, & more. This is a course for Business Enthusiasts who look for data-driven decision-making techniques for different business scenarios. This would provide a basic and intermediate level understanding of different Machine Learning Algorithms and how they can be implemented in KNIME. It would also teach the students how to judge the different Machine Learning Algorithms and which ones will fit your business scenario. KNIME is free and powerful software that has a vast number of business use cases.

Over the past decade, wind energy has gained more attention in the world. However, owing to its indirectness and volatility properties, wind power penetration has increased the difficulty and complexity in dispatching and planning of electric power systems. Therefore, it is needed to make the high-precision wind power prediction in order to balance the electrical power. For this purpose, in this study, the prediction performance of linear regression, k-nearest neighbor regression and decision tree regression algorithms is compared in detail. k-nearest neighbor regression algorithm provides lower coefficient of determination values, while decision tree regression algorithm produces lower mean absolute error values. In addition, the meteorological parameters of wind speed, wind direction, barometric pressure and air temperature are evaluated in terms of their importance on the wind power parameter. The biggest importance factor is achieved by wind speed parameter. In consequence, many useful assessments are made for wind power predictions.

In this tutorial will show you how to write a Python program that predicts the price of stocks using two different Machine Learning Algorithms, one is called a Support Vector Regression (SVR) and the other is Linear Regression. So you can start trading and making money! Actually this program is really simple and I doubt any major profit will be made from this program, but it's slightly better than guessing! In this video will show you how to write a Python program that predicts the price of stocks using two different Machine Learning Algorithms, one is called a Support Vector Regression (SVR) and the other is Linear Regression. So you can start trading and making money!

Developing an accurate prediction model for housing prices is always needed for socio-economic development and well-being of citizens. In this paper, a diverse set of machine learning algorithms such as XGBoost, CatBoost, Random Forest, Lasso, Voting Regressor, and others, are being employed to predict the housing prices using public available datasets. The housing datasets of 62,723 records from January 2015 to November 2019 are obtained from Florida Volusia County Property Appraiser website. The records are publicly available and include the real estate or economic database, maps, and other associated information. The database is usually updated weekly according to the State of Florida regulations. Then, the housing price prediction models using machine learning techniques are developed and their regression model performances are compared. Finally, an improved housing price prediction model for assisting the housing market is proposed. Particularly, a house seller or buyer, or a real estate broker can get insight in making better-informed decisions considering the housing price prediction. The empirical results illustrate that based on prediction model performance, Coefficient of Determination (R2), Mean Square Error (MSE), Mean Absolute Error (MAE), and computational time, the XGBoost algorithm performs superior to the other models to predict the housing price.

A question a lot of ML practitioners get asked a frequently is: "What can I do to start being able to actually build Machine Learning projects and solutions?" There is so much information out there -- both good and bad -- that it can be hard to know where to begin. Also, people come from very different backgrounds, so the starting point can vary significantly. For example, for me, I entered the ML world by watching theoretical videos from Computer Science channels about neural networks, and as I got more and more interested I started reading articles, news, and blogs about the topic. However, by doing this I only developed a vague understanding of the most superficial part of Machine Learning, and I was nowhere near being able to tackle a project by myself.

A question a lot of ML practitioners get asked a frequently is: "What can I do to start being able to actually build Machine Learning projects and solutions?" There is so much information out there -- both good and bad -- that it can be hard to know where to begin. Also, people come from very different backgrounds, so the starting point can vary significantly. For example, for me, I entered the ML world by watching theoretical videos from Computer Science channels about neural networks, and as I got more and more interested I started reading articles, news, and blogs about the topic. However, by doing this I only developed a vague understanding of the most superficial part of Machine Learning, and I was nowhere near being able to tackle a project by myself.

Scientific Computing relies on executing computer algorithms coded in some programming languages. Given a particular available hardware, algorithms speed is a crucial factor. There are many scientific computing environments used to code such algorithms. Matlab is one of the most tremendously successful and widespread scientific computing environments that is rich of toolboxes, libraries, and data visualization tools. OpenCV is a (C++)-based library written primarily for Computer Vision and its related areas. This paper presents a comparative study using 20 different real datasets to compare the speed of Matlab and OpenCV for some Machine Learning algorithms. Although Matlab is more convenient in developing and data presentation, OpenCV is much faster in execution, where the speed ratio reaches more than 80 in some cases. The best of two worlds can be achieved by exploring using Matlab or similar environments to select the most successful algorithm; then, implementing the selected algorithm using OpenCV or similar environments to gain a speed factor.