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Clearing air around “Boosting”
We explain the reasoning behind the massive success of boosting algorithms, how it came to be and what we can expect from them in the future.
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How to use continual learning in your ML models, June 19 Webinar
This webinar for professional data scientists will go over how to monitor models when in production, and how to set up automatically adaptive machine learning.
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Why organizations fail in scaling AI and Machine Learning
We explain why AI needs to understand business processes and how the business processes need to be able to change to bring insight from AI into the process.
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Your Guide to Natural Language Processing (NLP)
This extensive post covers NLP use cases, basic examples, Tokenization, Stop Words Removal, Stemming, Lemmatization, Topic Modeling, the future of NLP, and more.
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Building a Computer Vision Model: Approaches and datasets
How can we build a computer vision model using CNNs? What are existing datasets? And what are approaches to train the model? This article provides an answer to these essential questions when trying to understand the most important concepts of computer vision.
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 60+ useful graph visualization libraries
We outline 60+ graph visualization libraries that allow users to build applications to display and interact with network representations of data.
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A complete guide to K-means clustering algorithm
Clustering - including K-means clustering - is an unsupervised learning technique used for data classification. We provide several examples to help further explain how it works.
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Building Recommender systems with Azure Machine Learning service
Microsoft has provided a GitHub repository with Python best practice examples to facilitate the building and evaluation of recommendation systems using Azure Machine Learning services.
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Customer Churn Prediction Using Machine Learning: Main Approaches and Models
We reach out to experts from HubSpot and ScienceSoft to discuss how SaaS companies handle the problem of customer churn prediction using Machine Learning.
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How (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls
We outline some of the common pitfalls of machine learning for time series forecasting, with a look at time delayed predictions, autocorrelations, stationarity, accuracy metrics, and more.
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60+ useful graph visualization libraries
How (not) to use Machine Learning for time series forecasting: Avoiding the pitfalls