Introduction to Python Ensembles

Ensembles have rapidly become one of the hottest and most popular methods in applied machine learning. Virtually every winning Kaggle solution features them, and many data science pipelines have ensembles in them. Put simply, ensembles combine predictions from different models to generate a final prediction, and the more models we include the better it performs. Better still, because ensembles combine baseline predictions, they perform at least as well as the best baseline model. Ensembles give us a performance boost almost for free!


Alert Command Center enhances ability to monitor critical business transactions

With billions of transactions running through the OpenText™ Business Network platform from tens of thousands of B2B customers, the Business Network Support (BNS) team monitors the flow of your critical business documents each minute of every day. We support a reliable B2B network by monitoring your data flows with your trading partners worldwide to ensure the delivery of all business documents such as purchase orders, acknowledgements, shipping notifications, financial documents, sales reports, delivery notices, invoices, and inventory reports. If a document is not compliant with business rules or protocols it will notify the BNS team to take action. A failed transaction can have far-reaching consequences and for example, could shut down an automotive assembly line, delay a company’s payroll, result in the inability to meet a delivery commitment, delay a mortgage application, or delay a pharmacist to fill a prescription. We understand your business depends on our ability to deliver each document with speed and quality. We pride ourselves for having an extremely reliable support team, committed to maintaining and continually improving our high customer service ratings. The volumes of transactions we monitor and follow-up are increasing, as our customers adopt more digital B2B services. In addition, customers are going through their own digital evolutions and challenging us to perform our outmost to remedy and notify them with more speed and more information about any document delivery failure. These ever-increasing volumes of B2B messages and the availability of new technologies and solutions require us to rethink the way we are structured and retool our processes. OpenText is investing in a solution that will integrate more closely with customer business processes and evolve with their needs. It is time for a change with a new monitoring support model and a new digital strategy!


What Exactly is Artificial Intelligence and Why is it Driving me Crazy

Advanced analytic platform developers, cloud providers, and the popular press are promoting the idea that everything we do in data science is AI. That may be good for messaging but it’s misleading to the folks who are asking us for AI solutions and make


22 Great Articles and Tutorials on Classification Methods

• Time series classification with Tensorflow
• On Building a “Fake News” Classification Model +
• Data Science Simplified Part 10: An Introduction to Classification …
• Comparing classification algorithms: pluses and minuses
• Walmart Competition: Trip Type Classification
• Learning Binary Classification by Simulations
• Dogs vs. Cats: Image Classification with Deep Learning using TensorFlow in Python
• Introduction to Classification & Regression Trees (CART)
• Implementation of 17 classification algorithms in R
• Periodic Table of Binary Classification Performance Measures/Metrics
• Naive Bayes Classification explained with Python code
• Image Classification With HSV Color Model Processing
• Decision Trees, Classification & Interpretation Using SciKit-Learn
• Loan Prediction – Using PCA and Naive Bayes Classification with R
• Classification with scikit-learn
• Text Classification & Sentiment Analysis tutorial
• 5 Text Classification Case Studies Using SciKit Learn
• A Semi-Supervised Classification Algorithm using Markov Chain and Random Walk in R
• How to determine the quality and correctness of classification mode…
• Impact of target class proportions on accuracy of classification
• Experimenting with AWS Machine Learning for Classification
• 14 Great Articles and Tutorials on Clustering


Machine Learning Model Metrics

In this article we explore how to calculate machine learning model metrics, using the example of fraud detection. We’ll see lots of different ways that we can try to understand just how good our learned model is.


TensorFlow brings AI to the connected device

Consumers are beginning to expect more AI-driven interactions with their devices, whether they are interacting with smart assistants or expecting more tailored content within an application. However, when considering the landscape of available AI-focused applications, the list is significantly biased toward manufacturers. So, how can a third-party app developer provide an experience that is similar in performance and interactivity to built-in AI’s like Siri or Google Assistant? This is why the release of TensorFlow Lite is so significant. TensorFlow released TensorFlow Lite, this past November as an evolution of TensorFlow Mobile. TensorFlow Lite aims to alleviate some of the barriers to successfully implementing machine learning on a mobile device for third-party developers. The out-of-the-box version of TensorFlow can certainly develop models that are leveraged by mobile applications, but depending on the model’s complexity and size of the input data, the actual computation with current versions may be more efficient off of the device.


Tidyverse and data.table, sitting side by side… and then base R walks in

Of course, I’m paraphrasing Dirk’s fifteenth post in the rarely rational R rambling series: #15: Tidyverse and data.table, sitting side by side … (Part 1). I very much liked it, because, although I’m a happy tidyverse user, I’m always trying not to be tied into that verse too much by replicating certain tasks with other tools (and languages) as an exercise. In this article, I’m going to repeat Dirk’s exercise in base R.
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