Here you’ll find all articles that use machine learning, whether it’s classic machine learning such as random forests or logistic regression, or deep learning.
Machine learning is a generic term for the “artificial” generation of knowledge from experience. This means that an artificial system learns from examples by recognizing patterns and regularities. For this purpose, algorithms build a statistical model based on training data. Such a system can also assess unknown data and make own assumptions. There are two groups of machine learning algorithms: supervised learning and unsupervised learning. In supervised learning, the training process is based on prelabeled data. This means that the algorithm can compare its predictions to the correct answers. In this way, the algorithm learns from errors by adjusting the statistical model. In contrast, in unsupervised learning, the training data is unlabeled. Therefore, the algorithm does not know if its predictions are right or wrong and thus needs to come to own conclusions.
Online shopping has become a part of our daily lives, and online stores are continually seeking to improve their sales. One way to achieve this is by using machine learning to predict customers’ purchase intentions. This innovative process can help businesses understand their customers’ behavior and tailor their marketing strategies accordingly. In this article, we … Read more
Evaluating performance is a crucial step in developing regression models. Because regression models return continuous outputs, such models allow for different gradations of right or wrong. Therefore, we measure the deviation between predictions and actual values in numerical terms. However, a universal metric to measure the performance of regression models does not exist. Instead, there … Read more
Many time forecasting problems can be solved by predicting just one step into the future. However, some problems require a forecast for an extended period of time, which calls for a multi-step time series forecasting approach. This approach involves modeling the distribution of future values of a signal over a prediction horizon. In this article, … Read more
Get ready to level up your time-series forecasting game! In this tutorial, we’re going to take things up a notch by showing you how to adjust prediction intervals using Keras recurrent neural networks and Python. Now, you may remember our previous article on stock market forecasting where we made a forecast for the S&P500 stock … Read more
Financial analysts have long been fascinated by the prospect of predicting the prices of financial assets. In recent years, there has been increasing interest in using machine learning and deep learning techniques to generate predictions, in addition to traditional methods such as technical and fundamental analysis. Python libraries like Keras and Scikit-Learn make it relatively … Read more
If you travel a lot, you’ve probably already experienced this – you’re in a hurry on your way to the airport trying to catch a flight, only to find out that your flight is delayed. Wasn’t it great to know when a flight will be delayed in advance? We can use past flight delay data … Read more
