IBM Big Data Hub

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is machine learning? This post will dive deeper into the nuances of each field.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

AWS Big Data

MARCH 4, 2024

Lake Formation helps you centrally manage, secure, and globally share data for analytics and machine learning. Data files in snapshots are stored in one or more manifest files that contain a row for each data file in the table, its partition data, and its metrics. The following diagram illustrates this hierarchy.

Snapshot 113

Snapshot Data Lake Metadata Recreation/Entertainment 113

Domino Data Lab

JULY 22, 2019

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”. But for most enterprise, using machine learning…not really.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

The Unofficial Google Data Science Blog

JUNE 30, 2016

The field of statistical machine learning provides a solution to this problem, allowing exploration of larger spaces. An excellent review of statistical learning methods may be found in Friedman et. Random forest with default R tuning parameters (Breiman, 2001). Machine learning 45.1 2001): 5-32.

Statistics 44

Statistics Optimization Modeling Experimentation 44

Domino Data Lab

MAY 20, 2021

Machine Learning algorithms often need to handle highly-imbalanced datasets. def get_neigbours(M, k): nn = NearestNeighbors(n_neighbors=k+1, metric="euclidean").fit(M) When we say that a classification dataset is imbalanced, we usually mean that the different classes included in the dataset are not evenly represented.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

The Unofficial Google Data Science Blog

AUGUST 27, 2019

Of course, any mistakes by the reviewers would propagate to the accuracy of the metrics, and the metrics calculation should take into account human errors. If we could separate bad videos from good videos perfectly, we could simply calculate the metrics directly without sampling. The missing verdicts create two problems.

Metrics 98

Metrics Statistics Uncertainty Optimization 98