Dec
2019 Jan Opinions
All (103) | Courses, Education (8) | Meetings (12) | News (10) | Opinions (28) | Top Stories, Tweets (10) | Tutorials, Overviews (28) | Webcasts & Webinars (7)
- What Is Dimension Reduction In Data Science? - Jan 31, 2019.
An extensive introduction into Dimension Reduction, including a look at some of the different techniques, linear discriminant analysis, principal component analysis, kernel principal component analysis, and more.
- The Algorithms Aren’t Biased, We Are - Jan 29, 2019.
We explain the concept of bias and how it can appear in your projects, share some illustrative examples, and translate the latest academic research on “algorithmic bias.”
- Cracking the Data Scientist Interview - Jan 29, 2019.
After interviewing with over 50 companies for Data Scientist/Machine Learning Engineer, I am going to frame my experiences in the Q&A format and try to debunk any myths that beginners may have in their quest for becoming a Data Scientist.
- Monetizing the Math – are you ready? - Jan 28, 2019.
We outline an extensive list of things to do or plan for to help fully realize the ROI of your AI and Machine Learning projects in 2019.
- AI is a Big Fat Lie - Jan 26, 2019.
Is AI legit? This treatise by Eric Siegel, which ridicules the widespread myth of artificial intelligence, is enlightening and actually pretty funny. It's time for the term AI to be “terminated.”
-
Your AI skills are worth less than you think - Jan 25, 2019.
We are in the middle of an AI boom. That doesn’t mean that making your AI startup succeed is easy. I think there are some important pitfalls ahead of anyone trying to build their business around AI. - The Data Science Gold Rush: Top Jobs in Data Science and How to Secure Them - Jan 24, 2019.
Because big data touches almost every industry across the board, those who aren’t already working in data and analytics will soon be utilizing the technology for its undeniable business benefits. Whichever way you slice it, the future of work is through data.
-
What were the most significant machine learning/AI advances in 2018? - Jan 22, 2019.
2018 was an exciting year for Machine Learning and AI. We saw “smarter” AI, real-world applications, improvements in underlying algorithms and a greater discussion on the impact of AI on human civilization. In this post, we discuss some of the highlights. - How AI and Data Science is Changing the Utilities Industry - Jan 22, 2019.
Together, artificial intelligence (AI) and data science are causing positive developments for the utilities providers that choose to investigate these things. Here are some examples of technology at work.
- Data Science and Ethics – Why Companies Need a new CEO (Chief Ethics Officer) - Jan 21, 2019.
We explain why data science companies need to have a Chief Ethics Officer and discuss their importance in tackling algorithm bias.
- Cartoon: Is this how you do the blockchain thing? - Jan 19, 2019.
Despite the increasing popularity of Blockchain, the concept remains hard to understand. Here is one attempt to explain it.
- Why Ice Cream Is Linked to Shark Attacks – Correlation/Causation Smackdown - Jan 19, 2019.
Why are soda and ice cream each linked to violence? This article delivers the final word on what people mean by "correlation does not imply causation."
- How to Monitor Machine Learning Models in Real-Time - Jan 18, 2019.
We present practical methods for near real-time monitoring of machine learning systems which detect system-level or model-level faults and can see when the world changes.
- Data Scientist’s Dilemma: The Cold Start Problem – Ten Machine Learning Examples - Jan 17, 2019.
We present an array of examples showcasing the cold-start problems in data science where the algorithms and techniques of machine learning produce the good judgment in model progression toward the optimal solution.
- 10 Exciting Ideas of 2018 in NLP - Jan 16, 2019.
We outline a selection of exciting developments in NLP from the last year, and include useful recent papers and images to help further assist with your learning.
-
How to go from Zero to Employment in Data Science - Jan 15, 2019.
We propose the quickest and surest way to go from zero experience to landing a job, either in data science generally, or specifically in a new programming language or a new technology. - The 6 Most Useful Machine Learning Projects of 2018 - Jan 15, 2019.
Let’s take a look at the top 6 most practically useful ML projects over the past year. These projects have published code and datasets that allow individual developers and smaller teams to learn and immediately create value.
- Top Active Blogs on AI, Analytics, Big Data, Data Science, Machine Learning – updated - Jan 14, 2019.
Stay up-to-date with the latest technological advancements using our extensive list of active blogs; this is a list of 100 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.
- Why Vegetarians Miss Fewer Flights – Five Bizarre Insights from Data - Jan 12, 2019.
A frenzy of number-crunching is churning out a heap of insights that are colorful, sometimes surprising, and often valuable. We explain how this works, and investigate five bizarre discoveries found in data.
- The year in AI/Machine Learning advances: Xavier Amatriain 2018 Roundup - Jan 11, 2019.
A summary of the main machine learning advances from 2018, including AI hype cooling down, interpretability, deep learning, NLP, and more.
- Explainable Artificial Intelligence - Jan 10, 2019.
We outline the necessity of explainable AI, discuss some of the methods in academia, take a look at explainability vs accuracy, investigate use cases, and more.
- The Role of the Data Engineer is Changing - Jan 10, 2019.
The role of the data engineer in a startup data team is changing rapidly. Are you thinking about it the right way?
- 4 Myths of Big Data and 4 Ways to Improve with Deep Data - Jan 9, 2019.
There is a fundamental misconception that bigger data produces better machine learning results. However bigger data lakes / warehouses won’t necessarily help to discover more profound insights. It is better to focus on data quality, value and diversity not just size. "Deep Data" is better than Big Data.
- Core Principles of Sustainable Data Science, Machine Learning and AI Product Development: Research as a core driver - Jan 9, 2019.
Regardless of the size of your organisation, if you are developing machine learning or AI products, the core asset you have is a research professional, data scientist or AI scientist, regardless of their academic background.
- A Non-Compromising Approach to Privacy-Preserving Personalized Services - Jan 8, 2019.
Could one even achieve both high privacy and high utility? Yes, and we explain how.
- The Five Best Data Visualization Libraries - Jan 7, 2019.
There are plenty of library options out there to make great visualizations. We outline five of the best, complete with code examples and explanations, that will enable you to create and build interactive visualizations.
- The cold start problem: how to build your machine learning portfolio - Jan 4, 2019.
This post outlines what makes a good machine leaning portfolio, with useful examples to help you begin to understand the type of project that gets noticed by big companies.
- Ensemble Learning: 5 Main Approaches - Jan 3, 2019.
We outline the most popular Ensemble methods including bagging, boosting, stacking, and more.
