Ever wondered, “what algorithm google uses to maximize its target ads revenue?”. What about the e-commerce websites which advocates you through options such as ‘people who bought this also bought this’. Or “How does Facebook automatically suggest us to tag friends in pictures”? The answer is Recommendation Engines. With the growing amount of information on world wide web and with significant rise number of users, it becomes increasingly important for companies to search, map and provide them with the relevant chunk of information according to their preferences and tastes. Companies nowadays are building smart and intelligent recommendation engines by studying the past behavior of their users. Hence providing them recommendations and choices of their interest in terms of “Relevant Job postings”, “Movies of Interest”, “Suggested Videos”, “Facebook friends that you may know” and “People who bought this also bought this” etc.

This post will explain some of the lessons learned (and others confirmed) throughout the development, while I walk you its construction. This will be done in an opinionated way, which means that I will often add a comment regarding the data analysis discipline as a whole, instead of approaching it as an arid series of steps.

We introduce the challenge of detecting semantically compatible words, that is, words that can potentially refer to the same thing (cat and hindrance are compatible, cat and dog are not), arguing for its central role in many semantic tasks. We present a publicly available data-set of human compatibility ratings, and a neural-network model that takes distributional embeddings of words as input and learns alternative embeddings that perform the compatibility detection task quite well.

The following is a comprehensive list of Data Science courses and resources that explain or teach skills within Data Science, such as machine learning, data mining, analytics, cleaning, visualization, scraping, using APIs to make data products, artificial intelligence, and much more. Please excuse our appearance. We want to keep the list here for your reference while improving it live, so you may notice some sections here may be inconsistent. We are in the middle of building out what will hopefully be a much better viewing experience! Also, we would like you to know that some of the links to courses here are affiliate links. By going through us to gain access to a course, LearnDataSci may receive a commission. Thank you in advance to anyone that purchases a course from here, we greatly appreciate the support.

People find it difficult to think about random variation. Our mind is more strongly geared towards recognizing patterns than randomness. In this blogpost, you can learn what random variation looks like, how to reduce it by running well-powered studies, and how to meta-analyze multiple small studies. This is a long read, and most educational if you follow the assignments. You’ll probably need about an hour. We’ll use R, and the R script at the bottom of this post (or download it from GitHub). Run the first section (sections are differentiated by # # # #) to install the required packages and change some setting. IQ tests have been designed such that the mean IQ of the entire population of adults is 100, with a standard deviation of 15. This will not be true for every sample we draw from the population. Let’s get a feel for what the IQ scores from a sample look like. Which IQ scores will people in our sample have?

Playing with rCharts package I had the idea of representing the list of 100 best love songs as a connected set of points which forms a heart. Songs can be seen putting mouse cursor over each dot.