Aug2019 Sep Opinions
All (69) | Courses, Education (1) | Meetings (2) | News (6) | Opinions (20) | Top Stories, Tweets (7) | Tutorials, Overviews (30) | Webcasts & Webinars (3)
- The Hidden Risk of AI and Big Data
- Sep 20, 2019.
With recent advances in AI being enabled through access to so much “Big Data” and cheap computing power, there is incredible momentum in the field. Can big data really deliver on all this hype, and what can go wrong?
- 5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python
- Sep 19, 2019.
“I want to learn machine learning and artificial intelligence, where do I start?” Here.
- Data Science is Boring (Part 1)
- Sep 18, 2019.
Read about how one data scientist copes with his boring days of deploying machine learning.
- Which Data Science Skills are core and which are hot/emerging ones?
- Sep 17, 2019.
We identify two main groups of Data Science skills: A: 13 core, stable skills that most respondents have and B: a group of hot, emerging skills that most do not have (yet) but want to add. See our detailed analysis.
- 5 Alternative Data Science Tools
- Sep 17, 2019.
What other creative tools for data science beyond Python and R can you use to make an impression? It's not about the tool -- it's about its impact.
- My journey path from a Software Engineer to BI Specialist to a Data Scientist
- Sep 16, 2019.
The career path of the Data Scientist remains a hot target for many with its continuing high demand. Becoming one requires developing a broad set of skills including statistics, programming, and even business acumen. Learn more about one person's experience making this journey, and discover the many resources available to help you find your way into a world of data science.
- Cartoon: Unsupervised Machine Learning?
- Sep 14, 2019.
New KDnuggets Cartoon looks at one of the hottest directions in Machine Learning and asks "Can Machine Learning be too unsupervised?"
- Many Heads Are Better Than One: The Case For Ensemble Learning
- Sep 13, 2019.
While ensembling techniques are notoriously hard to set up, operate, and explain, with the latest modeling, explainability and monitoring tools, they can produce more accurate and stable predictions. And better predictions can be better for business.
- There is No Free Lunch in Data Science
- Sep 12, 2019.
There is no such thing as a free lunch in life or data science. Here, we'll explore some science philosophy and discuss the No Free Lunch theorems to find out what they mean for the field of data science.
- Classification vs Prediction
- Sep 12, 2019.
It is important to distinguish prediction and classification. In many decision-making contexts, classification represents a premature decision, because classification combines prediction and decision making and usurps the decision maker in specifying costs of wrong decisions.
- Common Machine Learning Obstacles
- Sep 9, 2019.
In this blog, Seth DeLand of MathWorks discusses two of the most common obstacles relate to choosing the right classification model and eliminating data overfitting.
- BERT is changing the NLP landscape
- Sep 9, 2019.
BERT is changing the NLP landscape and making chatbots much smarter by enabling computers to better understand speech and respond intelligently in real-time.
- What’s the difference between analytics and statistics?
- Sep 6, 2019.
From asking the best questions about data to answering those questions with certainty, understanding the value of these two seemingly different professions is clarified when you see how they should work together.
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I wasn’t getting hired as a Data Scientist. So I sought data on who is.
- Sep 6, 2019.
Instead of focusing on skills thought to be required of data scientists, we can look at what they have actually done before. - 3 Ways to Manage Human Bias in the Analytics Process
- Sep 5, 2019.
Managing human bias is an important part of the analytics process. Learn about three areas to watch out for to ensure your models are as unbiased as possible.
- Automated Machine Learning: Just How Much?
- Sep 5, 2019.
This is an interview between Rosaria Silipo and data scientists Paolo Tamagnini, Simon Schmid and Christian Dietz, asking a few questions on the topic of automated machine learning from their point of view, and some interesting examples of its practical use.
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Advice on building a machine learning career and reading research papers by Prof. Andrew Ng
- Sep 5, 2019.
This blog summarizes the career advice/reading research papers lecture in the CS230 Deep learning course by Stanford University on YouTube, and includes advice from Andrew Ng on how to read research papers. - TensorFlow vs PyTorch vs Keras for NLP
- Sep 3, 2019.
These three deep learning frameworks are your go-to tools for NLP, so which is the best? Check out this comparative analysis based on the needs of NLP, and find out where things are headed in the future.
- 6 Tips for Building a Training Data Strategy for Machine Learning
- Sep 2, 2019.
Without a well-defined approach for collecting and structuring training data, launching an AI initiative becomes an uphill battle. These six recommendations will help you craft a successful strategy.
- Cartoon: Labor Day in the age of AI
- Sep 2, 2019.
KDnuggets cartoon looks at how AI will impact Labor Day in the year 2050.
