Global trends like fuel price fluctuations, geopolitical events and government regulations have a huge impact on the airline industry. In an industry at the mercy of unpredictable trends, it is all the more important for companies to gather, analyze and apply data intelligently to all business processes.
Some key use cases where AI will provide tons of value in the aviation industry are as follows:
1. Optimizing MRO via Preventive Maintenance
Added by Mahesh Kumar CV on May 4, 2019 at 10:00am — No Comments
Unlike supervised learning, unsupervised learning not working with labeled data, it is not showing the machine the correct answer. Instead, it is using different algorithms to let the machine create connections by studying and observing the data. Learn much of this through study and observation. Learning and improving by trial and error is the key to unsupervised learning.
However, the Knowledge Discovery process is the field of data mining is concerned with the development…Continue
Added by Ariful Islam on May 19, 2019 at 8:42am — No Comments
Added by Andreas Blumauer on May 21, 2019 at 5:33am — No Comments
When you give customers advice that can help them save some money, they will pay you back with loyalty, which is priceless. Interesting fact: Fareboom users started spending twice as much time per session within a month of the release of an airfare price forecasting feature. This tool continues to grow conversion for our partner.
Besides travel, price predictions find their application in various scenarios. Commodity traders, investors, construction developers, or energy generators…Continue
Added by Kateryna Lytvynova on May 22, 2019 at 7:30am — No Comments
Data Science, Machine Learning, Deep Learning, and Artificial Intelligence are some of the most heard about buzzwords in the modern analytical eco-space. The exponential growth of technology in this regard has simplified our lives and made us more machine dependent. The astonishing hype surrounding such technologies has prompted professionals from various disciples to hop on to the ship and consider analytics as their career option.
To master Data Science or Artificial Intelligence in…Continue
Added by Divya Singh on May 21, 2019 at 9:30pm — No Comments
This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on…Continue
Added by Vincent Granville on May 21, 2019 at 5:30pm — No Comments
Summary: If you are guiding your company’s digital journey, to what extent should you be advising them to adopt deep learning AI methods versus traditional and mature machine learning techniques.
Summary: This may be the golden age of deep learning but a lot can be learned by looking at where deep neural nets aren’t working yet. This can be a guide to calming the hype. It can also be a roadmap to future opportunities once these barriers are behind us.
Added by William Vorhies on November 18, 2018 at 11:14am — No Comments
Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link.
Added by Vincent Granville on May 19, 2019 at 3:00pm — No Comments
There is this very intriguing Enterprise Car Rental commercial on TV (I’m old school and still watch my commercials on a television) that shows a glimpse into future driving behaviors enabled by autonomous vehicles (see Figure 1).…Continue
Added by Bill Schmarzo on May 17, 2019 at 1:14am — No Comments
I am pleased to announce that my quantum simulator Qubiter (available at GitHub, BSD license) now has a native TensorFlow Backend-Simulator (see its class `SEO_simulator_tf`, the `tf` stands for TensorFlow). This complements Qubiter's original numpy simulator (contained in its class `SEO_simulator`). A small step for Mankind, a giant leap for me! Hip Hip Hurray!
This means that Qubiter can now…Continue
Added by Robert R. Tucci on May 19, 2019 at 11:30am — No Comments
One of our business units wants to target the competitors’ customers with personalized product/offer. To do that, Business needs to understand who are the prepaid/postpaid customer of the competitor to push the relevant and personalized product/offer and they don’t have this data. Now, this is a binary classification problem and we want to apply machine learning machine method to predict the likeliness of competitor customer to be prepaid or postpaid.
Steps in Data Science…Continue
Added by Ariful Islam on May 19, 2019 at 9:00am — No Comments
Gone are the days when insurance and nbfc sector was relying completely on manual processes. Today with the emergence of AI and data-driven business decision making coupled with the application of IoT technologies there is a radical transformation of business processes in insurance and other financial services, like straight through processing becoming mainstream.
Let us look at different use cases:
1. Insurance Models:
Consider the recent proliferation…Continue
Added by Mahesh Kumar CV on May 19, 2019 at 7:11am — No Comments
I was deputed to work at Lagos, Nigeria in 2011 to work for a telecom giant there. The project in hand was to develop customer analytics modules using SAS on customer's newly built Oracle data warehouse. We thought about developing following modules.
Added by Dr. Moloy De on May 17, 2019 at 4:31pm — No Comments
Emerging applications like machine learning (ML), big data analytics, and artificial intelligence (AI) has created the need for many companies to hire highly skilled and experienced work force. Demand for data scientists, ML engineers and data engineers is booming and will only increase in the next years. The January report from Indeed, one of the top job sites, showed a 29% increase in demand for data scientists year over year and a 344% increase since 2013.
Added by Chris Kachris on May 17, 2019 at 4:50am — No Comments
Larry Page's "PageRank" Graph Algorithm as applied to Google search changed the digital world forever. Learn more in this Free eBook: Graph Algorithms: Practical Examples in Apache Spark and Neo4j, By Mark Needham & Amy E. Hodler, Published by O'Reilly Media https://neo4j.com/graph-algorithms-book/
Watch: Improve ML Predictions using Graph Analytics…Continue
Added by David Vessie on May 16, 2019 at 2:52pm — No Comments
This is another spectacular property of the exponential distribution, and also the first time an explicit formula is obtained for the variance of the range, besides the uniform distribution. It has important consequences, and the result is also useful in applications.
The range R(n) associated with n independent random variables with an exponential distribution of parameter l…Continue
By Ajit Jaokar and Dan Howarth. With contributions from Ayse Mutlu.
Exclusively for Data Science Central members, with free access. You can download this book (PDF) here.
This tutorial began as a series of weekend workshops created by Ajit Jaokar and Dan Howarth. The idea was to work with a specific (longish) program such that we explore as much of it…Continue
Added by Vincent Granville on May 16, 2019 at 8:30am — No Comments
Confidence intervals (CIs) tell you how much uncertainty a statistic has. The intervals are connected to confidence levels and the two terms are easily confused, especially if you're new to statistics. Confidence Intervals in One Picture is an intro to CIs, and explains how each part interacts with margins of error and where the different components come…Continue
Added by Stephanie Glen on May 17, 2019 at 10:00am — No Comments
The lifecycle of data travels through six phases:
The lifecycle "wheel" isn't set in stone. While it's common to move through the phases in order, it's possible to move in either direction (i.e. forward, backward) at any stage in the cycle. Work can also happen in several phases at the same time, or you can skip over…Continue