CIO Business Intelligence

APRIL 25, 2022

An education in data science can help you land a job as a data analyst , data engineer , data architect , or data scientist. The course includes instruction in statistics, machine learning, natural language processing, deep learning, Python, and R. Remote courses are also available.

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

IBM Big Data Hub

NOVEMBER 29, 2023

They are already identifying and exploring several real-life use cases for synthetic data, such as: Generating synthetic tabular data to increase sample size and edge cases. You can combine this data with real datasets to improve AI model training and predictive accuracy. How to get started with synthetic data in watsonx.ai

Metrics 85

Metrics Machine Learning Risk Statistics 85

O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. Some of the best lessons are captured in Ron Kohavi, Diane Tang, and Ya Xu’s book: Trustworthy Online Controlled Experiments : A Practical Guide to A/B Testing.

Marketing 362

Marketing Experimentation Metrics Testing 362

O'Reilly on Data

DECEMBER 12, 2019

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. It’s a very simple and powerful idea: simulate data that you find interesting and see what a model predicts for that data. 6] Debugging may focus on a variety of failure modes (i.e.,

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

DataRobot Blog

MAY 26, 2022

These methods provided the benefit of being supported by rich literature on the relevant statistical tests to confirm the model’s validity—if a validator wanted to confirm that the input predictors of a regression model were indeed relevant to the response, they need only to construct a hypothesis test to validate the input.

Risk 52

Risk Modeling Metrics Business Objectives 52

O'Reilly on Data

MARCH 31, 2020

Pragmatically, machine learning is the part of AI that “works”: algorithms and techniques that you can implement now in real products. We won’t go into the mathematics or engineering of modern machine learning here. After training, the system can make predictions (or deliver other results) based on data it hasn’t seen before.

Management 332

Management Machine Learning Experimentation Metrics 332

Smarten

APRIL 19, 2024

The value of an AI-focused analytics solution can only be fully realized when a business has ensured data quality and integration of data sources, so it will be important for businesses to choose an analytics solution and service provider that can help them achieve these goals.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57