FineReport

DECEMBER 19, 2019

When it comes to data analysis, from database operations, data cleaning, data visualization , to machine learning, batch processing, script writing, model optimization, and deep learning, all these functions can be implemented with Python, and different libraries are provided for you to choose. From Google.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature. Among these, only statistical uncertainty has formal recognition.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

Ontotext

APRIL 3, 2024

W hen you try to automate business processes, semantics, and machine learning , knowledge graphs can bring a lot of value. Nimit Mehta: I think that 2024 is going to be a buckle-down year, but, at the same time, we’ll see a rapid explosion of experimentation. These are not statistical inferences. What is a customer?

Technology 59

Technology Risk Digital Transformation Measurement 59

Insight

APRIL 28, 2020

How will they interact with product, engineering, sales, or marketing? you’re looking for a collaborator who can work and communicate well with you and your team, as well as anyone else that interacts with your team. What to evaluate: How will this person contribute to your culture? Will they be a strategic thought partner?

Data-driven 53

Data-driven Data Science Experimentation Measurement 53

Domino Data Lab

DECEMBER 4, 2020

Domino Lab supports both interactive and batch experimentation with all popular IDEs and notebooks (Jupyter, RStudio, SAS, Zeppelin, etc.). We can group by study arm and calculate various statistics as mean and standard deviation. The analyses shown below are accessible in the NCA project on Domino’s trial site.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Be aware that machine learning often involves working on something that isn’t guaranteed to work.

Management 110

Management Machine Learning Experimentation Metrics 110

Insight

MARCH 12, 2020

The most powerful approach for the first task is to use a ‘language model’ (LM), i.e. a statistical model of natural language. After some experimentation, I landed on a strategy I’ll call ‘warm encoding’: if greater than 1% of tags were in a particular class, I encoded the book as belonging to that class, non-exclusively.

Modeling 59

Modeling Metadata Publishing Sales 59

Data Science and Beyond

JULY 22, 2018

Two years later, I published a post on my then-favourite definition of data science , as the intersection between software engineering and statistics. It’s not all about machine learning. It is now much easier to deploy machine learning models, even without a deep understanding of how they work.

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

Corinium

APRIL 25, 2019

For example auto insurance companies offering to capture real-time driving statistics from policy-holders’ cars to encourage and reward safe driving. Machine learning can keep up, by continually looking for trends and anomalies, or predictive analytics, that are interesting for the given use case.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

Domino Data Lab

MAY 8, 2019

I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Other good related papers include: “ Towards A Rigorous Science of Interpretable Machine Learning ”. Not yet, if ever.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Cloudera

NOVEMBER 25, 2020

Scale to provide 1,000s of researchers frictionless interaction with data. How can he make it easy to see statistics, and do calculations, on discovered commonalities, across structured and unstructured data? How can users drill down, in non-technical ways, to quickly interact with data that explains what correlations seem to matter?

Data Warehouse 57

Data Warehouse Unstructured Data Analytics Visualization 57

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

It is important to make clear distinctions among each of these, and to advance the state of knowledge through concerted observation, modeling and experimentation. These quasi-explanations usually involve large, real effects and interactions so complex that arguments based on them are often non-falsifiable.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

Domino Data Lab

AUGUST 26, 2019

This article provides an excerpt of “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E. Data scientists, machine learning (ML) researchers, and business stakeholders have a high-stakes investment in the predictive accuracy of models. Introduction.

Testing 79

Testing Modeling Machine Learning Metrics 79

CIO Business Intelligence

JULY 6, 2023

After completing MTech from Indian Statistical Institute, I started my career at Cognizant. One of the major changes is the shift from signature-based protection to behavior-based Machine Learning dependent solutions. What do you do to foster a culture of innovation and experimentation in your employees?

Digital Transformation 94

Digital Transformation Experimentation Recreation/Entertainment Technology 94

Domino Data Lab

AUGUST 22, 2019

Although it’s not perfect, [Note: These are statistical approximations, of course!] Note: In more technical machine learning terms, the cost function of the skip-gram architecture is to maximize the log probability of any possible context word from a corpus given the current target word.] Example 11.6 Example 11.9 Example 11.11

Deep Learning 111

Deep Learning Modeling Metrics Testing 111