Occam's Razor

MARCH 30, 2017

Machine Learning | Marketing. Machine Learning | Analytics. People tend to use these phrases almost interchangeably: Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning. Deep Learning is a specific ML technique. Machine Learning | Marketing.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

O'Reilly on Data

JUNE 18, 2019

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI AI starts with ‘good’ data” is a statement that receives wide agreement from data scientists, analysts, and business owners. Alex Ratner on “Creating large training data sets quickly”.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

IBM Big Data Hub

MARCH 27, 2024

Some companies attempt to estimate Scope 3 emissions by collecting data from suppliers and manually categorizing data, but progress is hindered by challenges such as large supplier base, depth of supply chains, complex data collection processes and substantial resource requirements.

Experimentation 81

Experimentation Modeling Deep Learning Data-driven 81

Sisense

APRIL 10, 2020

These support a wide array of uses, such as data analysis, manipulation, visualizations, and machine learning (ML) modeling. Some standard Python libraries are Pandas, Numpy, Scikit-Learn, SciPy, and Matplotlib. These libraries are used for data collection, analysis, data mining, visualizations, and ML modeling.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

IBM Big Data Hub

APRIL 22, 2024

The US City of Atlanta , for example, uses IBM data collection, machine learning and AI to monitor public transit tunnel ventilation systems and predict potential failures that could put passengers at risk. This will help advance progress by optimizing resources used.

IoT 85

IoT Internet of Things Cost-Benefit Reporting 85

CIO Business Intelligence

MARCH 30, 2022

For organizations looking to move beyond stale reports, decision intelligence holds promise, giving them the ability to process large amounts of data with a sophisticated mix of tools such as artificial intelligence and machine learning to transform data dashboards and business analytics into more comprehensive decision support platforms.

Machine Learning 144

Machine Learning Testing Risk Data-driven 144

Smart Data Collective

APRIL 25, 2022

If you are relying on simple manual tracking processes, then this can be difficult to stay on topic due to the large amounts of online information and data about your business which is out there. Instead, machine learning algorithms can be much more effective when tracking online reviews. The Creation of Content on Social Media.

Management 135

Management Machine Learning Marketing Deep Learning 135

Ontotext

DECEMBER 15, 2023

The official (first) repo is tensorflow/tensor2tensor that has topics: machine-learning reinforcement-learning deep-learning machine-translation tpu. By exploring the first topic machine-learning , we find 117k Github repos. uie paddlenlp ). By exploring the topic nlp , we find 26.7k

Machine Learning 69

Machine Learning Interactive Deep Learning Statistics 69

CIO Business Intelligence

FEBRUARY 10, 2023

Predictive analytics definition Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning.

Predictive Analytics 145

Predictive Analytics Analytics Predictive Modeling Forecasting 145

DataRobot

OCTOBER 20, 2021

In order to scale responsible AI, organizations should implement these fundamental building blocks of data literacy: The data science and machine learning workflow: Learning about the steps required to create predictions from raw data helps stakeholders develop an understanding of AI project implementation.

Machine Learning 52

Machine Learning Metrics Risk Deep Learning 52

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

IBM Big Data Hub

SEPTEMBER 6, 2023

AI marketing is the process of using AI capabilities like data collection, data-driven analysis, natural language processing (NLP) and machine learning (ML) to deliver customer insights and automate critical marketing decisions. What is AI marketing?

Marketing 86

Marketing Technology Advertising ROI 86

IBM Big Data Hub

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data.

Data mining 58

Data mining Machine Learning Deep Learning Marketing 58

Decision Management Solutions

FEBRUARY 8, 2021

As you’ll see, the development of this amazing, one-of-a-kind vessel led to a conclusion that we at Decision Management Solutions see every day in our client work: It’s never enough to just rely on artificial intelligence (AI)/machine learning (ML) to do all the decision-making.

Machine Learning 93

Machine Learning Testing Deep Learning Optimization 93

bridgei2i

MARCH 9, 2021

Under the blanket of the collective term ‘AI technology,’ falls various technology segments, the most notable ones being – Natural Language Processing: The AI-powered machine’s ability to cognize and maneuver natural language just like humans. ML techniques provide better data-based outputs over time.

Technology 52

Technology Deep Learning Data-driven Optimization 52

IBM Big Data Hub

FEBRUARY 14, 2024

Using machine learning (ML), AI can understand what customers are saying as well as their tone—and can direct them to customer service agents when needed. These systems can evaluate vast amounts of data to uncover trends and patterns, and to make decisions.

Cost-Benefit 88

Cost-Benefit Insurance Unstructured Data Machine Learning 88

Smart Data Collective

MAY 8, 2022

Let’s not forget that big data and AI can also automate about 80% of the physical work required from human beings, 70% of the data processing, and more than 60% of the data collection tasks. From the statistics shown, this means that both AI and big data have the potential to affect how we work in the workplace.

Big Data 107

Big Data Data Analytics Analytics Cost-Benefit 107

DataRobot Blog

JULY 27, 2022

However, machine learning utilization is often unpredictable, which makes scaling sometimes a huge challenge. Investing in AI/ML is no longer an option but is critical for organizations to remain competitive. Many engineering teams don’t pay the necessary attention to it. The Demo: Autoscaling with MLOps.

Metrics 52

Metrics Deep Learning Modeling Machine Learning 52

Domino Data Lab

JULY 22, 2019

data science’s emergence as an interdisciplinary field – from industry, not academia. why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

IBM Big Data Hub

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. Marketing and sales: Conversational AI has become an invaluable tool for data collection. This data can be used to better understand customer preferences and tailor marketing strategies accordingly.

Enterprise 106

Enterprise Interactive Data-driven Key Performance Indicator 106

Rocket-Powered Data Science

JULY 13, 2023

Beyond the early days of data collection, where data was acquired primarily to measure what had happened (descriptive) or why something is happening (diagnostic), data collection now drives predictive models (forecasting the future) and prescriptive models (optimizing for “a better future”).

Analytics 166

Analytics Machine Learning Data Science Deep Learning 166

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.

Machine Learning 279

Machine Learning Data-driven IoT Deep Learning 279

Cloudera

NOVEMBER 19, 2020

In financial services, another highly regulated, data-intensive industry, some 80 percent of industry experts say artificial intelligence is helping to reduce fraud. Machine learning algorithms enable fraud detection systems to distinguish between legitimate and fraudulent behaviors.

Machine Learning 114

Machine Learning Data-driven Modeling Deep Learning 114

Cloudera

OCTOBER 1, 2022

While the word “data” has been common since the 1940s, managing data’s growth, current use, and regulation is a relatively new frontier. . Governments and enterprises are working hard today to figure out the structures and regulations needed around data collection and use.

Data Governance 71

Data Governance Strategy Enterprise Machine Learning 71

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. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Machine learning model interpretability.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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

IBM Big Data Hub

MAY 17, 2023

They use drones for tasks as simple as aerial photography or as complex as sophisticated data collection and processing. The services are activated through access management for data collection, analysis and event monitoring in existing drones which are managed by clients and businesses. billion in 2022 to USD 47.38

Data Collection 84

Data Collection Visualization Data-driven Unstructured Data 84

datapine

NOVEMBER 19, 2019

An important part of artificial intelligence comprises machine learning, and more specifically deep learning – that trend promises more powerful and fast machine learning. An exemplary application of this trend would be Artificial Neural Networks (ANN) – the predictive analytics method of analyzing data.

Technology 279

Technology Internet of Things IT IoT 279

IBM Big Data Hub

NOVEMBER 14, 2023

Data products and data mesh Data products are assembled data from sources that can serve a set of functional needs that can be packaged into a consumable unit. Each data product has its own lifecycle environment where its data and AI assets are managed in their product-specific data lakehouse.

Strategy 113

Strategy Cost-Benefit Data-driven Measurement 113

Insight

JANUARY 14, 2020

In short, I was faced with two major difficulties regarding data collection: I didn’t have nearly enough images, and the images I did have were not representative of a realistic gym environment. On top of this issue, I only had between 40–50 unique images for each class. Check out the Keras documentation here.

Metrics 58

Metrics Optimization Modeling Testing 58

Domino Data Lab

OCTOBER 9, 2019

We’ll examine National Oceanic and Atmospheric Administration (NOAA) data management practices which I learned about at their workshop, as a case study in how to handle data collection, dataset stewardship, quality control, analytics, and accountability when the stakes are especially high. How cool is that?!

Deep Learning 69

Deep Learning Metadata Machine Learning Data Science 69

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. Welcome back to our monthly series about data science! This month, the theme is not specifically about conference summaries; rather, it’s about a set of follow-up surveys from Strata Data attendees.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

O'Reilly on Data

MARCH 24, 2020

Beyond the autonomous driving example described, the “garbage in” side of the equation can take many forms—for example, incorrectly entered data, poorly packaged data, and data collected incorrectly, more of which we’ll address below. The model and the data specification become more important than the code.

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346

Occam's Razor

OCTOBER 12, 2017

Intro to Machine Learning. Machine Learning. Deep Learning. For Digital Analysts, it is creating data collection mechanisms, writing queries, creating reports, doing segmentation, creating rules, identifying business focus areas based on data etc. All three of these courses are free: 1.

Marketing 136

Marketing Analytics Machine Learning Strategy 136