Mlearning.ai

MAY 5, 2023

These models, which are based on artificial intelligence and machine learning algorithms, are designed to process vast amounts of natural language data and generate new content based on that data. This includes things like text preprocessing, part-of-speech tagging, parsing, and sentiment analysis.

Natural Language Processing 98

Natural Language Processing Deep Learning Deep Learning Exploratory Data Analysis 98

Dataconomy

SEPTEMBER 22, 2023

Data archiving is the systematic process of securely storing and preserving electronic data, including documents, images, videos, and other digital content, for long-term retention and easy retrieval. This system provides long-term storage at a lower cost than primary storage systems.

Clustering 185

Clustering Algorithm Data Classification Machine Learning 185

Women in Big Data

DECEMBER 13, 2023

With a robust educational foundation in Computer Science, Mathematics, and Statistics, she brings over 12 years of expertise across Research, Academia, and Industry. Currently based in Germany, she possesses extensive experience in developing data-intensive applications leveraging NLP, data science, and data analytics.

Natural Language Processing 52

Natural Language Processing Big Data Big Data Computer Science 52

IBM Journey to AI blog

APRIL 18, 2024

Imagine a world where machines aren’t confined to pre-programmed tasks but operate with human-like autonomy and competence. AI systems like LaMDA and GPT-3 excel at generating human-quality text, accomplishing specific tasks, translating languages as needed, and creating different kinds of creative content.

AI 97

AI AI Computer Science Computer Science 97

ODSC - Open Data Science

JANUARY 29, 2024

Using skills such as statistical analysis and data visualization techniques, prompt engineers can assess the effectiveness of different prompts and understand patterns in the responses. Prompt Engineering: Favored Programming Languages While Python’s dominance comes as no surprise, the presence of R and other languages raises an eyebrow.

Data Science 52

Data Science Machine Learning Machine Learning Natural Language Processing 52

AWS Machine Learning Blog

MAY 25, 2023

Collecting usage statistics. He is also an adjunct lecturer in the MS data science and analytics program at Georgetown University in Washington D.C. He has published many papers in ACL, ICDM, KDD conferences, and Royal Statistical Society: Series A. streamlit run webapp.py sagemaker-user@studio$ streamlit run webapp.py

AWS 77

AWS Clustering Python ML 77

Explosion

AUGUST 3, 2017

This is good, because the examples are how you program the behaviour – the learner itself is really just a compiler. To produce the annotation manuals, you need to know what statistical models will be required for the features you’re trying to build. How easy would it be to create a bot to tag the issues automatically?

Supervised Learning 52

Supervised Learning Python Machine Learning Machine Learning 52