Dec
2017 Jan Opinions, Interviews
http likes 49All (107) | Courses, Education (12) | Meetings (15) | News, Features (23) | Opinions, Interviews (26) | Software (3) | Tutorials, Overviews (25) | Webcasts & Webinars (3)
- Avoiding Another AI Winter
- Jan 30, 2017.
This post is a look at the factors -- public fears and a loss of investor appetite -- that could thwart AI progress... if we don’t pay them enough attention.
- Bad Data + Good Models = Bad Results
- Jan 26, 2017.
No matter how advanced is your Machine Learning algorithm, the results will be bad if the input data
is bad. We examine one popular IMDB dataset and discuss how an analyst can deal with such data. -
6 areas of AI and Machine Learning to watch closely
- Jan 25, 2017.
Artificial Intelligence is a generic term and many fields of science overlaps when comes to make an AI application. Here is an explanation of AI and its 6 major areas to be focused, going forward. - Bringing Business Clarity To CRISP-DM
- Jan 24, 2017.
Many analytic projects fail to understand the business problem they are trying to solve. Correctly applying decision modeling in the Business Understanding phase of CRISP-DM brings clarity to the business problem.
- The Data Science Puzzle, Revisited
- Jan 20, 2017.
The data science puzzle is re-examined through the relationship between several key concepts in the realm, and incorporates important updates and observations from the past year. The result is a modified explanatory graphic and rationale.
- Data Science of Sales Calls: 3 Actionable Findings
- Jan 19, 2017.
How does AI help sales and marketing teams in the organisation? Let’s understand Dos and don’ts of sales calls with the help of analysis of over 70,000+ B2B SaaS sales calls.
- Four Problems in Using CRISP-DM and How To Fix Them
- Jan 18, 2017.
CRISP-DM is the leading approach for managing data mining, predictive analytic and data science projects. CRISP-DM is effective but many analytic projects neglect key elements of the approach.
- Data Scientist New Year Resolutions for 2017
- Jan 17, 2017.
Do you make any new year resolutions? Hit the gym more often? Lose that last 10 pounds? While personal resolutions often get a bad rap, setting professional goals at the start of the new year is not necessarily a bad idea. Check out one data scientist's new year resolutions for 2017.
- More Data or Better Algorithms: The Sweet Spot
- Jan 17, 2017.
We examine the sweet spot for data-driven Machine Learning companies, where is not too easy and not too hard to collect the needed data.
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Deep Learning Can be Applied to Natural Language Processing
- Jan 16, 2017.
This post is a rebuttal to a recent article suggesting that neural networks cannot be applied to natural language given that language is not a produced as a result of continuous function. The post delves into some additional points on deep learning as well. - Exclusive: Interview with Jeremy Howard on Deep Learning, Kaggle, Data Science, and more
- Jan 14, 2017.
My exclusive interview with rock star Data Scientist Jeremy Howard, on his latest Deep Learning course, what is needed for success in Kaggle, how Enlitic is transforming medical diagnostics, and what Data Scientists should do to create value for their organization.
- Clean Data Science: Evaluating The Cleanliness of NYC Craft Beer Bar Kitchens
- Jan 13, 2017.
An analysis of NYC Open Data health inspections showing that craft beer bar kitchens in Manhattan are cleaner than the average establishment by a statistically significant margin. An encouraging finding for Dry January.
- Data Hoarding and Alternative Data In Finance – How to Overcome the Challenges
- Jan 13, 2017.
Big data craze inspires firms to save every possible bit of data, with the misconception that the more data you have, the better. Firms must keep data (for compliance purposes) or often aren’t sure what information they need to keep. This post looks at alternative data sources.
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Big Data and the Internet of Things don’t make business smarter, Analytics and Data Science do
- Jan 12, 2017.
Big Data does not convert data into actionable information. Big Data does not create value. But Data Science does, and it does not have to be complex or expensive, or even big. - The Most Popular Language For Machine Learning and Data Science Is …
- Jan 11, 2017.
When it comes to choosing programming language for Data Analytics projects or job prospects, people have different opinions depending on their career backgrounds and domains they worked in. Here is the analysis of data from indeed.com with respect to choice of programming language for machine learning and data science.
- Text Mining Amazon Mobile Phone Reviews: Interesting Insights
- Jan 10, 2017.
We analyzed more than 400 thousand reviews of unlocked mobile phones sold on Amazon.com to find out insights with respect to reviews, ratings, price and their relationships.
- A Non-comprehensive List of Awesome Things Other People Did in 2016
- Jan 10, 2017.
A top statistics professor and statistical researcher reflects on a number of awesome accomplishments by individuals in, and related to, the fields of statistics and data science, with a focus on the world of academia but with resonance far beyond.
- Social Media for Marketing and Healthcare: Focus on Adverse Side Effects
- Jan 9, 2017.
Social media like twitter, facebook are very important sources of big data on the internet and using text mining, valuable insights about a product or service can be found to help marketing teams. Lets see, how healthcare companies are using big data and text mining to improve their marketing strategies.
- arXiv Paper Spotlight: Sampled Image Tagging and Retrieval Methods on User Generated Content
- Jan 9, 2017.
Image tagging with user generated content in the wild, without the use of curated image datasets? Read more about this paper and its promising research.
- A Tasty approach to data science
- Jan 7, 2017.
Data scientists at Foodpairing help brands cut down on the fuzzy front end of product development. The so-called Consumer Flavor Intelligence combines internet data and food science to create timely flavor line extensions.
- Machine Learning Meets Humans – Insights from HUML 2016
- Jan 6, 2017.
Report from an important IEEE workshop on Human Use of Machine Learning, covering trust, responsibility, the value of explanation, safety of machine learning, discrimination in human vs. machine decision making, and more.
- The Major Advancements in Deep Learning in 2016
- Jan 5, 2017.
Get a concise overview of the major advancements observed in deep learning over the past year.
- How To Stay Competitive In Machine Learning Business
- Jan 4, 2017.
To stay competitive in machine learning business, you have to be superior than your rivals and not the best possible – says one of the leading machine learning expert. Simple rules are defined here to make that happen. Let’s see how.
- Revenue per Employee: golden ratio, or red herring?
- Jan 4, 2017.
There is growing support for revenue per employee as one of the most underrated metrics available for assessing business performance in a crowded marketplace.
- Ten Myths About Machine Learning, by Pedro Domingos
- Jan 3, 2017.
Myths on artificial intelligence and machine learning abound. Noted expert Pedro Domingos identifies and refutes a number of these myths, of both the pessimistic and optimistic variety.
- Uber-fication! Uberize Your Business
- Jan 2, 2017.
We examine what Uber has done that drives success in many markets across the globe and why so many businesses are seeking an Uber-style solution to their business. We present a listing of lessons on what to do if you are seeking to Uber-ize your business model.
Learn How to Listen: One of the hardest parts of being a data scientist
An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning