Time series forecasting is an important area of machine learning that is often neglected. It is important because there are so many prediction problems that involve a time component. These problems are neglected because it is this time component that makes time series problems more difficult to handle.
Get more familiar with creating machine learning predictive algorithms on Time series data
Now that you’re machine learning algorithm has finished learning from the data obtained using Python or R, you’re looking the results from your test dataset and wondering whether you can improve them or have reached the best possible outcome. There are a number of techniques you can use to improve machine learning performance and achieve a more general predictor that’s able to work equally well with your test set or new data.
Here I discuss techniques and metrics that helped me in improving my Machine learning models during my work.
Clustering can be considered the most important unsupervised machine learning problem; so, like every other problem of this kind, it deals with finding a structure in a collection of unlabeled data.
A loose definition of clustering could be “the process of organizing objects into groups whose members are similar in some way”.
A cluster is, therefore, a collection of objects which are “similar” between them and are “dissimilar” to the objects belonging to other clusters.
In order to go in-depth on what exactly data science and machine learning (ML) tools or platforms are, why companies small and large are moving toward them, and why they matter in the Enterprise AI journey, it’s essential to take a step back and understand where we are in the larger story of AI, ML, and data science in the context of businesses