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MaherBlockedUnblockFollowFollowing Feb 15 There is a 5-Step shortcut that you can do to be able to solve machine learning problems right away, As a beginner, you can take this path at first if you want to get something done with machine learning. And then you can take the 10-steps path
In the first part, you learned about trends and seasonality, smoothing models and ARIMA processes. In this part, you’ll learn how to deal with seasonal models and how to implement Seasonal Holt-Winters and Seasonal ARIMA (SARIMA). Getting the data We’ll use the “Monthly milk production” data: Seasonal decomposition (TLS) In the
I have been working with Tensorflow during last months and I realized that, although there is a large number of Github repositories with many different and complex models, is hard to find a simple example that shows you how to obtain your own dataset from the web and apply some
Introduction In this article, I’m interviewing a veteran data scientist, Dr Stylianos (Stelios) Kampakis, about his career to date and how he helps decision makers across a range of businesses understand how data science can benefit them. Dr Stelios Kampakis (image: his own) While data science is a field showing immense
Assessing the uncertainty of predictions is elementary for business decisions. Mixture density networks help you to better understand the uncertainty you are facing in the real world. Introduction Uncertainty is a key element in every decision we make. In business, however, managers regularly face decisions entailing a wide variety of unforeseeable
TDS TeamBlockedUnblockFollowFollowing Feb 15 A Gentle Introduction to Graph Neural Networks (Basics, DeepWalk, and GraphSage) By 黃功詳 Steeve Huang — 8 min read Recently, Graph Neural Network (GNN) has gained increasing popularity in various domains, including social network, knowledge graph, recommender system, and even life science.
An Step-by-Step Guide for Building an Anti-Semitic Tweet Classifier Text + Intelligence = Gold… But, how can we mine it cheaply? Introduction There is a catch to training state-of-the-art NLP models: their reliance on massive hand-labeled training sets. That’s why data labeling is usually the bottleneck in developing NLP applications and
In this guide we’ll look at methods from the os and shutil modules. The os module is the primary Python module for interacting with the operating system. The shutil module also contains high-level file operations. For some reason you make directories with os but move and copy them with shutil.
This project is inspired by Drivetime Sedans case study from the book “Marketing Data Science: Modelling Techniques in Predictive Analytics with R and Python” by Thomas Miller. The author only provides the dataset and problem statements. You can have access to the dataset and my R code here. 1. Introduction
Some tips in finding the right target for optimization, and how to figure it out for your use case In one of his books, Isaac Asimov envisions a future where computers have become so intelligent and powerful, that they are able to answer any question. In that future, Asimov postulates, scientists