Nov2018 Dec Opinions
All (51) | Courses, Education (3) | Meetings (3) | News (8) | Opinions (14) | Top Stories, Tweets (6) | Tutorials, Overviews (13) | Webcasts & Webinars (4)
- NLP Breakthrough Imagenet Moment has arrived
- Dec 14, 2018.
A comprehensive review of the current state of Natural Language Processing, covering the process from shallow to deep pre-training, what's in an ImageNet, the case for language modelling, and more.
- Why You Shouldn’t be a Data Science Generalist
- Dec 14, 2018.
But it’s hard to avoid becoming a generalist if you don’t know which common problem classes you could specialize in in the fist place. That’s why I put together a list of the five problem classes that are often lumped together under the “data science” heading.
- Four Approaches to Explaining AI and Machine Learning
- Dec 12, 2018.
We discuss several explainability techniques being championed today, including LOCO (leave one column out), permutation impact, and LIME (local interpretable model-agnostic explanations).
- Machine Learning & AI Main Developments in 2018 and Key Trends for 2019
- Dec 11, 2018.
As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Machine Learning and AI experts as to the most important developments of 2018 and their 2019 key trend predictions.
- Learning Machine Learning vs Learning Data Science
- Dec 11, 2018.
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.
- Should you become a data scientist?
- Dec 10, 2018.
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.
- How Different are Conventional Programming and Machine Learning?
- Dec 10, 2018.
When I heard about Machine Learning I couldn't contain the amazement. I was not able to get my mind around the fact, that unlike normal software programs - which I was accustomed to - I wouldn't even have to teach a computer the "how" in detail about all the future scenarios up front.
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Here are the most popular Python IDEs / Editors
- Dec 7, 2018.
We report on the most popular IDE and Editors, based on our poll. Jupyter is the favorite across all regions and employment types, but there is competition for no. 2 and no. 3 spots. - 6 Step Plan to Starting Your Data Science Career
- Dec 5, 2018.
When people want to launch data science careers but haven't made the first move, they're in a scenario that's understandably daunting and full of uncertainty. Here are six steps to get started.
- Kick Start Your Data Career! Tips From the Frontline
- Dec 5, 2018.
I am going to provide very interesting and useful tips through this blog series which will help students to kick start their career in Data.
- Data Science Projects Employers Want To See: How To Show A Business Impact
- Dec 4, 2018.
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.
- Why Primary Research?
- Dec 4, 2018.
Primary studies have always been a strength of marketing research. Many younger marketing researchers, however, have only been exposed to standardized ready-made research products or big data. This is a concern. What is the point of the word research in marketing research?
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AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019
- Dec 3, 2018.
Review of 2018 and Predictions for 2019 from our panel of experts, including Meta Brown, Tom Davenport, Carla Gentry, Bob E Hayes, Cassie Kozyrkov, Doug Laney, Bill Schmarzo, Kate Strachnyi, Ronald van Loon, Favio Vazquez, and Jen Underwood. - Best Machine Learning languages, Data Visualization Tools, DL Frameworks, and Big Data Tools
- Dec 3, 2018.
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.