This article was written by Will Koehrsen.
Reading through a data science book or taking a course, it can feel like you have the individual pieces, but don’t quite know how to put them together. Taking the next step and solving a complete machine learning problem can be daunting, but preserving and completing a first… Continue
Added by Andrea Manero-Bastin on May 20, 2019 at 6:30am —
This article was written by James Le.
Some examples of tasks best solved by machine learning include:
- Recognizing patterns: Objects in real scenes, Facial identities or facial expressions, Spoken words.
- Recognizing anomalies: Unusual sequences of credit card transactions, Unusual patterns of sensor readings in a nuclear power…
Added by Andrea Manero-Bastin on May 20, 2019 at 6:00am —
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 —
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 —
This is not very simple to choose a machine learning method and letting it go wild on the data. Particularly, understanding the core business problem and objective of the outcome and frame accordingly is one of the vital factors in machine learning. A general approach is difficult to recommend without intimate knowledge of the data. However, it sounds like we need to formalize the aspects of your model. Following questions may help to decide on machine learning problem or… Continue
Added by Ariful Islam on May 19, 2019 at 9:00am —
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 —
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 —
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 —
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
Added by Vincent Granville on May 19, 2019 at 6:30am —
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.
- Customer Churn Analysis
- Calculating Product Propensities
- Customer Lifetime Value Calculation
- Customer Segmentation
Customer Churn… Continue
Added by Dr. Moloy De on May 17, 2019 at 4:31pm —
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 from.…
Added by Stephanie Glen on May 17, 2019 at 10:00am —
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).…
Added by Bill Schmarzo on May 17, 2019 at 1:14am —
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 —
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 —
I was given 3 GB of Machine Generated data being fed by 120 sensors (5 records every second) in an excel format. The task in hand was to mine out interesting patterns, if any, from the data.
I fed the data in R in my local machine and performed various descriptive and exploratory analysis to have some insights. Customer was also looking for some low cost maintenance mechanisms for their machines. So I thought if I could study the outliers and provide some information about system… Continue
Added by Dr. Moloy De on May 15, 2019 at 7:30pm —
Machine learning applications require powerful and scalable computing systems that can sustain the high computation complexity of these applications. Companies that are working on the domain of machine learning have to allocate a significant amount of their budget for the OpEx of machine learning applications whether this is done on cloud or on-prem.
Typical machine learning application… Continue
Added by Chris Kachris on May 14, 2019 at 11:30pm —
PyTorch is known for being a clean framework, and hence it was a challenging task to achieve the production capability and flexibility needed for research. I think that the major hurdle for pushing production support to the core was going out of Python's realm and moving the PyTorch model to a faster, thread-safe language that has multithreading capability. But then, that violated the Python-first principle that PyTorch had up to that… Continue
Added by Packt Publishing on May 14, 2019 at 9:48pm —
The supply chain of today is governed by the manufacturer, wholesaler, retailer, and businesses operating in between. Not only has the concept of a supply chain progressed by leaps and bounds during the last decade or so, but it is also one of the most critical…
Added by Ronald van Loon on May 14, 2019 at 9:11pm —
The Future of Investment Banking
In this article, we look at how technology such as robotic automation, artificial intelligence and machine learning is changing and benefiting the investment banking industry.
A quick guide to investment banking
Banks are made up of different areas. They have retail banking, which is about providing loans, bank accounts and other services to the general…
Added by Harrison Goode on May 14, 2019 at 8:49am —
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 13, 2019 at 10:00pm —