Opinions, Interviews, Reports
We welcome guest blogs and opinions - see submission guidelines.
Latest:
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Why software engineering processes and tools don’t work for machine learning - Dec 5, 2019.
While AI may be the new electricity significant challenges remain to realize AI potential. Here we examine why data scientists and teams can’t rely on software engineering tools and processes for machine learning. -
The Essential Toolbox for Data Cleaning - Dec 5, 2019.
Increase your confidence to perform data cleaning with a broader perspective of what datasets typically look like, and follow this toolbox of code snipets to make your data cleaning process faster and more efficient. -
Artificial Friend or Virtual Foe - Dec 5, 2019.
Is AI making more good than harm? -
Explainability: Cracking open the black box, Part 1 - Dec 4, 2019.
What is Explainability in AI and how can we leverage different techniques to open the black box of AI and peek inside? This practical guide offers a review and critique of the various techniques of interpretability. -
KDnuggets Poll: How well do current AutoML solutions work? - Dec 4, 2019.
Take part in our latest poll, asking readers their opinions on the effectiveness of current automated machine learning solutions. -
A Non-Technical Reading List for Data Science - Dec 2, 2019.
The world still cannot be reduced to numbers on a page because human beings are still the ones making all the decisions. So, the best data scientists understand the numbers and the people. Check out these great data science books that will make you a better data scientist without delving into the technical details. -
Top 7 Data Science Use Cases in Trust and Security - Dec 2, 2019.
What are trust and safety? What is the role of trust and security in the modern world? Read this overview of 7 data science application use cases in the realm of trust and security. -
Google Open Sources MobileNetV3 with New Ideas to Improve Mobile Computer Vision Models - Dec 2, 2019.
The latest release of MobileNets incorporates AutoML and other novel ideas in mobile deep learning.
November:
- Two Years In The Life of AI, Machine Learning, Deep Learning and Java
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Open Source Projects by Google, Uber and Facebook for Data Science and AI
Open source is becoming the standard for sharing and improving technology. Some of the largest organizations in the world namely: Google, Facebook and Uber are open sourcing their own technologies that they use in their workflow to the public. - Cartoon: Thanksgiving, Big Data, and Turkey Data Science…
- A Doomed Marriage of Machine Learning and Agile
- Task-based effectiveness of basic visualizations
- Would you buy insights from this guy? (How to assess and manage a Data Science vendor)
- Top 8 Data Science Use Cases in Marketing
- Can Neural Networks Develop Attention? Google Thinks they Can
- Advice for New and Junior Data Scientists
- The Notebook Anti-Pattern
- Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
- Why write for KDnuggets? Calling for original blogs and new authors
- Reproducibility, Replicability, and Data Science
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Data Science for Managers: Programming Languages
In this article, we are going to talk about popular languages for Data Science and briefly describe each of them. - On the sensationalism of artificial intelligence news
- How I Got Better at Machine Learning
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How to Speed up Pandas by 4x with one line of code
While Pandas is the library for data processing in Python, it isn't really built for speed. Learn more about the new library, Modin, developed to distribute Pandas' computation to speedup your data prep.
- How Data Analytics Can Assist in Fraud Detection
- What is Data Science?
- The Last Defense Against Another AI Winter
- How to Become a Successful Healthcare Data Analyst
