CIO Business Intelligence

MAY 20, 2022

The Machine Learning Department at Carnegie Mellon University was founded in 2006 and grew out of the Center for Automated Learning and Discovery (CALD), itself created in 1997 as an interdisciplinary group of researchers with interests in statistics and machine learning. Carnegie Mellon University.

Machine Learning 145

Machine Learning Statistics Recreation/Entertainment Data Science 145

Smart Data Collective

JUNE 10, 2022

The Bureau of Labor Statistics reports that there are over 105,000 data scientists in the United States. Machine Learning Engineer. As a machine learning engineer, you would create data funnels and deliver software solutions. Machine Learning Scientist. Are you interested in a career in data science?

Data Science 119

Data Science Machine Learning Statistics Business Intelligence 119

datapine

AUGUST 14, 2019

To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations. It helps to automate and makes the usage of the R programming statistical language easier and much more effective. perfect for statistical computing and design.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

datapine

MAY 14, 2019

Best for: Those looking for a practical means of understanding how artificial intelligence serves to enhance data science and use this knowledge to improve their data analytics strategies. 2) “Deep Learning” by Ian Goodfellow, Yoshua Bengio and Aaron Courville. 4) “Machine Learning Yearning” by Andrew Ng.

Data Science 350

Data Science Machine Learning Data-driven Big Data 350

O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. We won’t go into the mathematics or engineering of modern machine learning here.

Management 332

Management Machine Learning Experimentation Metrics 332

Cloudera

FEBRUARY 25, 2021

Today’s enterprise data science teams have one of the most challenging, yet most important roles to play in your business’s ML strategy. In our current landscape, businesses that have adopted a successful ML strategy are outperforming their competitors by over 9%. The work of a machine learning model developer is highly complex.

Deep Learning 83

Deep Learning Machine Learning Forecasting Visualization 83

IBM Big Data Hub

APRIL 22, 2024

We believe there are three core areas that every organization should focus on: sustainability strategy and reporting; energy transition and climate resilience; and intelligent asset, facility and infrastructure management. This approach can help organizations to more easily establish a sustainability strategy across the business.

IoT 83

IoT Internet of Things Cost-Benefit Reporting 83

bridgei2i

OCTOBER 7, 2020

Adoption of Automated Sales & Underwriting Strategies can Transform Insurance. Underwriting essentially means evaluating the risk proposition of an insurance cover and determining how much an insurer should charge for the same by studying historical data and statistical models. The way ahead for insurers.

Insurance 52

Insurance Sales Strategy Digital Transformation 52

Occam's Razor

MARCH 30, 2017

Machine Learning | Marketing. Machine Learning | Analytics. My personal strategy: Understand reality. Invest in continuous learning. People tend to use these phrases almost interchangeably: Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning. Rinse and repeat.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

bridgei2i

FEBRUARY 21, 2020

In fact, statistics from Maryville University on Business Data Analytics predict that the US market will be valued at more than $95 billion by the end of this year. Deep learning provides an edge over your competition. Forbes predicts that predictive analytics will ensure that companies get a much-needed edge this year.

IoT 83

IoT Analytics Deep Learning B2B 83

Domino Data Lab

JULY 28, 2021

Through a marriage of traditional statistics with fast-paced, code-first computer science doctrine and business acumen, data science teams can solve problems with more accuracy and precision than ever before, especially when combined with soft skills in creativity and communication. Math and Statistics Expertise.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

CIO Business Intelligence

AUGUST 11, 2023

How natural language processing works NLP leverages machine learning (ML) algorithms trained on unstructured data, typically text, to analyze how elements of human language are structured together to impart meaning. NLTK is offered under the Apache 2.0 It was primarily developed at the University of Massachusetts Amherst.

Unstructured Data 141

Unstructured Data Modeling Machine Learning Data mining 141

IBM Big Data Hub

FEBRUARY 13, 2024

Generative AI represents a significant advancement in deep learning and AI development, with some suggesting it’s a move towards developing “ strong AI.” Generative AI uses advanced machine learning algorithms and techniques to analyze patterns and build statistical models.

Enterprise 124

Enterprise Modeling Testing Data-driven 124

Smart Data Collective

MAY 8, 2022

From the statistics shown, this means that both AI and big data have the potential to affect how we work in the workplace. By doing this, businesses can form their finance & marketing strategies with the new information they have gathered. As a result, it can improve how effective it is and help you set up better strategies.

Big Data 107

Big Data Data Analytics Analytics Cost-Benefit 107

O'Reilly on Data

JULY 28, 2020

It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Products based on deep learning can be difficult (or even impossible) to develop; it’s a classic “high return versus high risk” situation, in which it is inherently difficult to calculate return on investment.

Marketing 362

Marketing Experimentation Metrics Testing 362

Rocket-Powered Data Science

JULY 6, 2023

While there has been huge public interest in generative AI (specifically, ChatGPT) by individuals, there has been a transformative impact on organizations everywhere, both in strategy conversations and tactical deployments.

Strategy 152

Strategy Enterprise Software Marketing 152

Domino Data Lab

DECEMBER 1, 2019

a change in the interest rate by a regulator, a natural calamity, the introduction of a new competitor in the market or a change in a business strategy/process, etc.). In the context of machine learning, we consider data drift 1 to be the change in model input data that leads to a degradation of model performance.

Modeling 77

Modeling Machine Learning Deep Learning Testing 77

IBM Big Data Hub

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. What is machine learning?

Machine Learning 80

Machine Learning Modeling Deep Learning Predictive Modeling 80

DataRobot Blog

MAY 26, 2022

Validating Modern Machine Learning (ML) Methods Prior to Productionization. In this post, we will dive deeper into how members from both the first and second line of defense within a financial institution can adapt their model validation strategies in the context of modern ML methods. Validating Machine Learning Models.

Risk 52

Risk Modeling Metrics Business Objectives 52

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. Introduction Machine learning models often behave unpredictably, as data scientists would be the first to tell you.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. While AGI promises machine autonomy far beyond gen AI, even the most advanced systems still require human expertise to function effectively.

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. Data poisoning attacks. Watermark attacks.

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

Smarten

APRIL 19, 2024

Assisted Predictive Modeling and Auto Insights to create predictive models using self-guiding UI wizard and auto-recommendations The Future of AI in Analytics The C=suite executive survey revealed that 93% felt that data strategy is critical to getting value from generative AI, but a full 57% had made no changes to their data.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

Domino Data Lab

AUGUST 10, 2021

At the center of every machine learning or artificial intelligence application is the ML/AI model that is built with data, algorithms and code. This change makes models the new currency of competitive advantage, strategy, and growth. PyTorch: used for deep learning models, like natural language processing and computer vision.

Modeling 64

Modeling IT Risk Management Deep Learning 64

Domino Data Lab

APRIL 21, 2021

In this article, we’ll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot anomalies that the human eye might not catch. In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Domino Data Lab

DECEMBER 17, 2019

Logistic regression, from a statistical perspective, is an example of a discriminative approach. Back-propagation algorithm or backprop, from a deep learning perspective, is an example of a successful discriminative approach. Semi-supervised learning is one strategy for reducing the number of labels.

Deep Learning 64

Deep Learning Machine Learning Modeling Statistics 64

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

CONTACT Software

FEBRUARY 19, 2019

In line with this, Gartner has now published a “ Magic Quadrant for Data Science and Machine Learning Platforms “ The document itself can only be viewed behind a paywall, but on the net some of the companies mentioned in the report offer access to the document by entering the address.

Data Science 40

Data Science Statistics Machine Learning Deep Learning 40

Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. O’Reilly Media published our analysis as free mini-books: The State of Machine Learning Adoption in the Enterprise (Aug 2018). The data types used in deep learning are interesting.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Domino Data Lab

JULY 9, 2019

Wiggins advocated that data scientists find problems that impact the business; re-frame the problem as a machine learning (ML) task; execute on the ML task; and communicate the results back to the business in an impactful way. I still believe that data science is the craft of trying to apply machine learning to some real world problem.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

O'Reilly on Data

JANUARY 25, 2021

We’re not pretending the frameworks themselves are comparable—Spring is primarily for backend and middleware development (though it includes a web framework); React and Angular are for frontend development; and scikit-learn and PyTorch are machine learning libraries. AI, Machine Learning, and Data.

Machine Learning 363

Machine Learning Software Testing Technology 363