Data Science 101

OCTOBER 31, 2019

The former is a term used for models where the data has been labeled, whereas, unsupervised learning, on the other hand, refers to unlabeled data. Classification is a form of supervised learning technique where a known structure is generalized for distinguishing instances in new data. Clustering. Classification.

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Dataconomy

MAY 25, 2023

This enables them to respond quickly to changing conditions or events. Here are some important machine learning techniques used in IoT: Supervised learning Supervised learning involves training machine learning models with labeled datasets.

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Pickl AI

JANUARY 2, 2025

Summary: Entropy in Machine Learning quantifies uncertainty, driving better decision-making in algorithms. It optimises decision trees, probabilistic models, clustering, and reinforcement learning. Entropy enhances clustering, federated learning, finance, and bioinformatics.

Machine Learning 52

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Smart Data Collective

JUNE 26, 2019

There are two essential classifiers for developing machine learning applications with this library: a supervised learning model known as an SVM and a Random Forest (RF). There are numerous reasons that scikit-learn is one of the preferred libraries for developing machine learning solutions.

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Pickl AI

NOVEMBER 4, 2024

Scikit-learn Scikit-learn is the go-to library for Machine Learning in Python. Scikit-learn covers various classification , regression , clustering , and dimensionality reduction algorithms. Local meetups offer opportunities to connect with peers, collaborate on projects, and learn from each other’s experiences.

Data Science 52

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Snorkel AI

APRIL 18, 2023

And then what they did is they came up with 18 different high-level climate change hazards that talk about climate trends–both long-term and short-term–and also extreme events that are indicators and precursors that affect our day-to-day living. To address all these problems, we looked into weak supervised learning.

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Snorkel AI

APRIL 18, 2023

And then what they did is they came up with 18 different high-level climate change hazards that talk about climate trends–both long-term and short-term–and also extreme events that are indicators and precursors that affect our day-to-day living. To address all these problems, we looked into weak supervised learning.

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Google Research AI blog

APRIL 30, 2023

Posted by Catherine Armato, Program Manager, Google The Eleventh International Conference on Learning Representations (ICLR 2023) is being held this week as a hybrid event in Kigali, Rwanda. We are proud to be a Diamond Sponsor of ICLR 2023, a premier conference on deep learning, where Google researchers contribute at all levels.

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Women in Big Data

DECEMBER 13, 2023

The event was part of the chapter’s technical talk series 2023. The Technical Talk Series focuses on Technical Skills, bringing awareness about a technical topic, sharing knowledge, and ways to learn/enhance required skills, thus linking it to career development. I look forward to attending future events hosted by WiBD”.

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Pickl AI

SEPTEMBER 12, 2024

Boosting: An ensemble learning technique that combines multiple weak models to create a strong predictive model. C Classification: A supervised Machine Learning task that assigns data points to predefined categories or classes based on their characteristics.

Data Analyst 52

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Pickl AI

AUGUST 9, 2024

Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers. Data Streaming Learning about real-time data collection methods using tools like Apache Kafka and Amazon Kinesis.

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DagsHub

DECEMBER 23, 2024

Balanced Dataset Creation Balanced Dataset Creation refers to active learning's ability to select samples that ensure proper representation across different classes and scenarios, especially in cases of imbalanced data distribution.   Supports batch processing for quick processing for the images. 

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Google Research AI blog

JULY 23, 2023

Elsayed , Aravindh Mahendran , Fisher Yu , Avital Oliver , Fantine Huot , Jasmijn Bastings , Mark Patrick Collier , Alexey Gritsenko , Vighnesh Birodkar , Cristina Vasconcelos , Yi Tay , Thomas Mensink , Alexander Kolesnikov , Filip Pavetić , Dustin Tran , Thomas Kipf , Mario Lučić , Xiaohua Zhai , Daniel Keysers , Jeremiah Harmsen , Neil Houlsby Fast (..)

Machine Learning 77

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Heartbeat

OCTOBER 25, 2023

Machine Learning Tools in Bioinformatics Machine learning is vital in bioinformatics, providing data scientists and machine learning engineers with powerful tools to extract knowledge from biological data. Clustering algorithms can group similar biological samples or identify distinct subtypes within a disease.

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The MLOps Blog

JANUARY 5, 2023

To get started, it is my pleasure to introduce you to our guest, machine learning and data science engineer Kuba Cieslik. It’s nice to participate in this event. I’m a machine learning engineer with some years of experience in building ML products and ML solutions. How self-supervised learning works.

Machine Learning 52

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DrivenData Labs

FEBRUARY 3, 2025

I generated unlabeled data for semi-supervised learning with Deberta-v3, then the Deberta-v3-large model was used to predict soft labels for the unlabeled data. The semi-supervised learning was repeated using the gemma2-9b model as the soft labeling model.

Machine Learning 130

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Google Research AI blog

NOVEMBER 28, 2022

Posted by Cat Armato, Program Manager, Google This week marks the beginning of the 36th annual Conference on Neural Information Processing Systems ( NeurIPS 2022 ), the biggest machine learning conference of the year.

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Dataconomy

MARCH 18, 2025

Understanding these anomalies is essential, as they can indicate significant events affecting decision-making processes. How anomaly detection works Understanding how anomaly detection works involves exploring different machine learning approaches. Supervised machine learning Supervised learning uses labeled datasets to train models.

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The MLOps Blog

MARCH 21, 2023

The service will consume the features in real time, generate predictions in near real-time , such as in an event processing pipeline, and write the outputs to a prediction queue. Orchestrators are concerned with lower-level abstractions like machines, instances, clusters, service-level grouping, replication, and so on.

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