- Save Sarah Connor with Data Science - Oct 25, 2021.
Data science and data privacy are deeply interwoven, and must be carefully considered by practitioners. In comparing the Safe Harbour and Expert Determination data obfuscation approaches, Safe Harbour has been very popular among data engineers but has fundamental limitations, where Expert Determination offers important advantages.
- Content-based Recommender Using Natural Language Processing (NLP) - Nov 26, 2019.
A guide to build a content-based movie recommender model based on NLP.
- GitHub Repo Raider and the Automation of Machine Learning - Nov 18, 2019.
Since X never, ever marks the spot, this article raids the GitHub repos in search of quality automated machine learning resources. Read on for projects and papers to help understand and implement AutoML.
- How Data Science Is Used Within the Film Industry - Jul 5, 2019.
As Data Science is becoming pervasive across so many industries, Hollywood is certainly not being left behind. Learn about how Big Data, analytics, and AI are now core drivers of the movies we watch and how we watch them.
- Building a Recommender System, Part 2 - Jul 3, 2019.
This post explores an technique for collaborative filtering which uses latent factor models, a which naturally generalizes to deep learning approaches. Our approach will be implemented using Tensorflow and Keras.
- Building a Recommender System - Apr 4, 2019.
A beginners guide to building a recommendation system, with a step-by-step guide on how to create a content-based filtering system to recommend movies for a user to watch.
- Connecting the dots for a Deep Learning App - Aug 31, 2017.
We show how to build a Deep Learning app which does sentiment analysis on movie reviews. Try it yourself!
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- Bad Data + Good Models = Bad Results - Jan 26, 2017.
No matter how advanced is your Machine Learning algorithm, the results will be bad if the input data
is bad. We examine one popular IMDB dataset and discuss how an analyst can deal with such data.
- If Hollywood Made Movies About Machine Learning Algorithms - Apr 1, 2016.
A lighthearted take on the kind of movie Hollywood would produce if it took on machine learning algorithms.
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- Top KDnuggets tweets, Mar 22-29: If Hollywood Made Movies About MachineLearning; Data Scientist on Every @AirBNB Leadership Team - Mar 30, 2016.
If Hollywood Made Movies About Machine Learning; Why Airbnb Has a Data Scientist on Every Leadership Team; Very useful guide for Data Cleaning in Python; Data scientist Hilary Mason wants to show you the (near) future.
- OpenText Data Visualization – Red Carpet Edition - Mar 4, 2016.
In the this latest edition we present handsome variation on the bubble chart, plotting numbers of nominations against Oscars won, and how many films fall into each category.
- Top KDnuggets tweets, Feb 22-28: Quantifying Similarity in Structured Data; #Oscar #DataScience: 4-5 nominations no guarantee of winning - Feb 29, 2016.
A Statistical View of #DeepLearning; Impressive tutorial - Tree Kernels: Quantifying Similarity in Structures; Conversation with Data Scientist Sebastian Raschka - new podcast; How to become a #Bayesian in eight easy steps.
- On Why Sequels Are Bad and Red Light Cameras Aren’t As Effective - Feb 3, 2016.
Regression to the mean is a statistical phenomenon whereby extreme observations will tend to decrease (regress) towards the mean on subsequent readings. Regression to the mean is essentially a result of selection bias, learn more about it.
- The Star Wars social networks – who is the central character? - Dec 25, 2015.
Data Scientist looks at the 6 Star Wars movies to extract the social networks, within each film and across the whole Star Wars universe. Network structure reveals some surprising differences between the movies, and finds who is actually the central character.
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- Which Movie Sequels Are Really Better? A Data Science Answer - Oct 19, 2015.
The internet is filled with polls and lists of sequels that are better or worse movie in the series. Yet such rankings are often based on personal judgement and rarely on data and statistics. Here is our solution to analyze and visualize the movie series.