Thoughts and Theory

By Joe Schneid at Wikimedia Commons

Most state-of-the-art (SOTA) time series classification methods are limited by high computational complexity. This makes them slow to train on smaller datasets and effectively unusable on large datasets.

Recently, ROCKET (RandOM Convolutional KErnel Transform) has achieved SOTA of accuracy in just a fraction of the time as other SOTA time…


Opinion

Image by UnboxScience at Pixabay

Conventional machine learning algorithms, such as linear regression and xgboost, operate in “batch” mode. That is, they fit a model using a full dataset in one go. Updating that model with new data requires fitting a brand new model from scratch using both the new data and the old data.


Photo by Joshua Dixon on Unsplash

Cross validation is a useful procedure to help select optimal hyperparameters for a machine learning model. It is especially useful for smaller datasets, where there is not enough data to create representative train, validation, and test sets. …


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There are many paths that lead to Medium success and I believe I have found one of them. I consistently average $150-$250 per month, excluding the $500 Medium bonuses. Once I started tagging my articles as #artificialintelligence, I earned the Top Writer in Artificial Intelligence tag. …


Image by geralt at pixabay

Sktime is a popular new python package for time series machine learning. The contributors continue to fix bugs and add new features — and invite you to contribute too!

Why contribute to sktime?

  1. Improve your skills in machine learning and coding.
  2. Learn the nuts-and-bolts of machine learning algorithms.
  3. Build your…


So you want to write a widely read article about Data Science / Machine Learning / Artificial Intelligence?

In May 2021, I was recognized as a top writer in AI and was among the top 1000 writers in the Medium Partner Program. My older articles still continue to receive views…


Machine Learning

Image by Hans at Pixabay

Outlier detection is a machine learning task that aims to identify rare items, events, or observations that deviate from the “norm” or general distribution of the given data.

An anomaly is something that arouses suspicion that it was generated by different data generating mechanism

The Outlier Detection Machine Learning Task

In the outlier detection task, the…


This is an exciting development! This will certainly be useful for many practitioners.


Photo by Anita Ritenour at flickr

PyOD is a Python library with a comprehensive set of scalable, state-of-the-art (SOTA) algorithms for detecting outlying data points in multivariate data. This task is commonly referred to as Outlier Detection or Anomaly Detection.

The outlier detection task aims to identify rare items, events, or observations that deviate from the…


Image by Marco Verch Professional Photographer

Outliers, or anomalies are data points that deviate from the norm of a dataset. They arouse suspicion that they were generated by a different mechanism.

Anomaly detection is (usually) an unsupervised learning task where the objective is to identify suspicious observations in data. …

Alexandra Amidon

Data scientist working in the financial services industry

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