Deep Learning, Predictive Modeling and Statistics

Beyond the hype: Do you really need an LLM for your data?

CIO Business Intelligence

FEBRUARY 6, 2025

Think about it: LLMs like GPT-3 are incredibly complex deep learning models trained on massive datasets. Even basic predictive modeling can be done with lightweight machine learning in Python or R. In life sciences, simple statistical software can analyze patient data. Theyre impressive, no doubt.

Unstructured Data

Unstructured Data Manufacturing Data Governance Sales

Data Science Subjects and Syllabus [Latest Topics Included]

Analytics Vidhya

MAY 26, 2023

Imagine diving into the details of data analysis, predictive modeling, and ML. Envision yourself unraveling the insights and patterns for making informed decisions that shape the future. The concept of Data Science was first used at the start of the 21st century, making it a relatively new area of research and technology.

Data Science

Data Science Predictive Modeling Modeling Technology

The quest for high-quality data

O'Reilly on Data

JUNE 18, 2019

The good news is that researchers from academia recently managed to leverage that large body of work and combine it with the power of scalable statistical inference for data cleaning. HoloClean adopts the well-known “noisy channel” model to explain how data was generated and how it was “polluted.”

Machine Learning

Machine Learning Data Quality Statistics Modeling

Webinars

How to Achieve High-Accuracy Results When Using LLMs

MORE WEBINARS

Proposals for model vulnerability and security

O'Reilly on Data

MARCH 20, 2019

The objective here is to brainstorm on potential security vulnerabilities and defenses in the context of popular, traditional predictive modeling systems, such as linear and tree-based models trained on static data sets. If an attacker can receive many predictions from your model API or other endpoint (website, app, etc.),

Modeling

Modeling Machine Learning Predictive Modeling Consulting

Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model.

Deep Learning

Deep Learning Modeling Metrics Testing

Top Data Science Tools That Will Empower Your Data Exploration Processes

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

Data Science Statistics Business Intelligence Visualization

Predictive Analytics: 4 Primary Aspects of Predictive Analytics

Smart Data Collective

SEPTEMBER 16, 2020

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data.

Predictive Analytics

Predictive Analytics Analytics Predictive Modeling Data mining

What is predictive analytics? Transforming data into future insights

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. from 2022 to 2028.

Predictive Analytics

Predictive Analytics Analytics Predictive Modeling Forecasting

15 best data science bootcamps for boosting your career

CIO Business Intelligence

APRIL 25, 2022

The data science path you ultimately choose will depend on your skillset and interests, but each career path will require some level of programming, data visualization, statistics, and machine learning knowledge and skills. On-site courses are available in Munich. Remote courses are also available. Switchup rating: 5.0 (out

Data Science

Data Science Machine Learning Deep Learning Statistics

What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

JUNE 7, 2022

The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance.

Data Analytics

Data Analytics Diagnostic Analytics Management Analytics

12 data science certifications that will pay off

CIO Business Intelligence

JANUARY 19, 2024

The US Bureau of Labor Statistics (BLS) forecasts employment of data scientists will grow 35% from 2022 to 2032, with about 17,000 openings projected on average each year. You need experience in machine learning and predictive modeling techniques, including their use with big, distributed, and in-memory data sets.

Data Science

Data Science Machine Learning Predictive Modeling Forecasting

The curse of Dimensionality

Domino Data Lab

OCTOBER 7, 2020

Statistical methods for analyzing this two-dimensional data exist. This statistical test is correct because the data are (presumably) bivariate normal. When there are many variables the Curse of Dimensionality changes the behavior of data and standard statistical methods give the wrong answers. Data Has Properties.

Statistics

Statistics Testing Predictive Modeling Modeling

11 most in-demand gen AI jobs companies are hiring for

CIO Business Intelligence

OCTOBER 13, 2023

Responsibilities include building predictive modeling solutions that address both client and business needs, implementing analytical models alongside other relevant teams, and helping the organization make the transition from traditional software to AI infused software.

Deep Learning

Deep Learning Machine Learning Modeling Software

Why you should care about debugging machine learning models

O'Reilly on Data

DECEMBER 12, 2019

More structured approaches to sensitivity analysis include: Adversarial example searches : this entails systematically searching for rows of data that evoke strange or striking responses from an ML model. Figure 1 illustrates an example adversarial search for an example credit default ML model.

Machine Learning

Machine Learning Modeling Testing Risk Management

3 Key Components of the Interdisciplinary Field of Data Science

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

Data Science Statistics Predictive Analytics Recreation/Entertainment

R vs Python: What’s the Best Language for Natural Language Processing?

Sisense

APRIL 10, 2020

R is a tool built by statisticians mainly for mathematics, statistics, research, and data analysis. Here, we will implement the XG-Boost algorithm, an algorithm that learns on the basis of training data (which we loaded earlier in both R and Python programming languages) with the help of probability and statistics.

Deep Learning

Deep Learning Data Science Machine Learning Visualization

AI In Analytics: Today and Tomorrow!

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

Analytics Predictive Modeling KPI Machine Learning

Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

JULY 6, 2023

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve.

Machine Learning

Machine Learning Data Science Statistics Deep Learning

Five machine learning types to know

IBM Big Data Hub

DECEMBER 20, 2023

ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions. In other words, ML leverages input data to predict outputs, continuously updating outputs as new data becomes available.

Machine Learning

Machine Learning Modeling Deep Learning Predictive Modeling

Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

Domino Data Lab

APRIL 21, 2021

Rules-based fraud detection (top) vs. classification decision tree-based detection (bottom): The risk scoring in the former model is calculated using policy-based, manually crafted rules and their corresponding weights. deep learning) there is no guaranteed explainability. It can be implemented as either unsupervised (e.g.

Statistics

Statistics Machine Learning Modeling Metrics

Data Science at The New York Times

Domino Data Lab

JULY 9, 2019

Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.

Data Science

Data Science Machine Learning Advertising Modeling

The unreasonable importance of data preparation

O'Reilly on Data

MARCH 24, 2020

HoloClean decouples the task of data cleaning into error detection (such as recognizing that the location “cicago” is erroneous) and repairing erroneous data (such as changing “cicago” to “Chicago”), and formalizes the fact that “data cleaning is a statistical learning and inference problem.”

Machine Learning

Machine Learning Statistics Data Quality Modeling

Data Leaders Brief

Beyond the hype: Do you really need an LLM for your data?

Data Science Subjects and Syllabus [Latest Topics Included]

Webinars

Trending Sources

The quest for high-quality data

Webinars

Proposals for model vulnerability and security

Deep Learning Illustrated: Building Natural Language Processing Models

Top Data Science Tools That Will Empower Your Data Exploration Processes

Predictive Analytics: 4 Primary Aspects of Predictive Analytics

What is predictive analytics? Transforming data into future insights

15 best data science bootcamps for boosting your career

What is data analytics? Analyzing and managing data for decisions

12 data science certifications that will pay off

The curse of Dimensionality

11 most in-demand gen AI jobs companies are hiring for

Why you should care about debugging machine learning models

3 Key Components of the Interdisciplinary Field of Data Science

R vs Python: What’s the Best Language for Natural Language Processing?

AI In Analytics: Today and Tomorrow!

Data science vs. machine learning: What’s the difference?

Five machine learning types to know

Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

Data Science at The New York Times

The unreasonable importance of data preparation

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