Category: AI/ML Research

Working with time series data often means wrestling with the same patterns over and over: calculating moving averages, detecting spikes, creating features for forecasting models.

When you have a small dataset, choosing the right machine learning model can make a big difference.

Perhaps one of the most underrated yet powerful features that scikit-learn has to offer, pipelines are a great ally for building effective and modular machine learning workflows.

In this article, you’ll learn to: • Turn unstructured, raw image data into structured, informative features.

If you’re reading this, it’s likely that you are already aware that the performance of a machine learning model is not just a function of the chosen algorithm.

These days, it is not uncommon to come across datasets that are too large to fit into random access memory (RAM), especially when working on advanced data analysis projects at scale, managing streaming data generated at high velocity, or building large machine learning models.

You’ve built a machine learning model that performs perfectly on training data but fails on new examples.

In classification models , failure occurs when the model assigns the wrong class to a new data observation; that is, when its classification accuracy is not high enough over a certain number of predictions.

NumPy is one of the most popular Python libraries for working with numbers and data.

Visualizing model performance is an essential piece of the machine learning workflow puzzle.