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State of Deep Learning and Major Advances: H2 2018 Review
In this post we summarise some of the key developments in deep learning in the second half of 2018, before briefly discussing the road ahead for the deep learning community.
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Four Approaches to Explaining AI and Machine Learning
We discuss several explainability techniques being championed today, including LOCO (leave one column out), permutation impact, and LIME (local interpretable model-agnostic explanations).
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Automated Web Scraping in R
How to automatically web scrape periodically so you can analyze timely/frequently updated data.
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 Learning Machine Learning vs Learning Data Science
We clarify some important and often-overlooked distinctions between Machine Learning and Data Science, covering education, scalable vs non-scalable jobs, career paths, and more.
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Should you become a data scientist?
An overview of the current situation for data scientists, from its origins and history, to the recent growth in job postings, and looking at what changes the future might bring.
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A comprehensive list of Machine Learning Resources: Open Courses, Textbooks, Tutorials, Cheat Sheets and more
By Dan Clark, KDnuggets on December 7, 2018 in Cheat Sheet, Data Science Education, Deep Learning, Machine Learning, Mathematics, Open Source, Reinforcement Learning, Resources, StatisticsA thorough collection of useful resources covering statistics, classic machine learning, deep learning, probability, reinforcement learning, and more.
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Common mistakes when carrying out machine learning and data science
We examine typical mistakes in Data Science process, including wrong data visualization, incorrect processing of missing values, wrong transformation of categorical variables, and more. Learn what to avoid!
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How to build a data science project from scratch
A demonstration using an analysis of Berlin rental prices, covering how to extract data from the web and clean it, gaining deeper insights, engineering of features using external APIs, and more.
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Data Science Projects Employers Want To See: How To Show A Business Impact
The best way to create better data science projects that employers want to see is to provide a business impact. This article highlights the process using customer churn prediction in R as a case-study.
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Best Machine Learning Languages, Data Visualization Tools, DL Frameworks, and Big Data Tools
We cover a variety of topics, from machine learning to deep learning, from data visualization to data tools, with comments and explanations from experts in the relevant fields.
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Learning Machine Learning vs Learning Data Science
Should you become a data scientist?