Data Science 101

OCTOBER 31, 2019

Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). 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. Regression.

Data Mining 77

Data Mining Data Mining Data Mining Data Science 77

Pickl AI

OCTOBER 24, 2024

Summary: This article compares Artificial Intelligence (AI) vs Machine Learning (ML), clarifying their definitions, applications, and key differences. While AI aims to replicate human intelligence across various domains, ML focuses on learning from data to improve performance.

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Artificial Intelligence Artificial Intelligence Machine Learning Machine Learning 52

Dataconomy

MARCH 27, 2023

Definition and purpose of RPA Robotic process automation refers to the use of software robots to automate rule-based business processes. Unsupervised learning:  This involves using unlabeled data to identify patterns and relationships within the data. Level of human intervention: Minimal human intervention required.

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ML ML Machine Learning Machine Learning 70

Pickl AI

JANUARY 2, 2025

Introduced by Claude Shannon in 1948, entropy revolutionised how we measure information and remains central to modern Data Science, including Machine Learning. Lets delve into its mathematical definition and key properties. Lets explore its definition, connection to entropy, and practical applications.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Clustering 52

IBM Journey to AI blog

SEPTEMBER 20, 2023

Large language models (LLMs) are a class of foundational models (FM) that consist of layers of neural networks that have been trained on these massive amounts of unlabeled data. Large language models (LLMs) have taken the field of AI by storm.

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Clustering AI AI Data Science 95

ODSC - Open Data Science

FEBRUARY 14, 2023

ChatGPT is a next-generation language model (referred to as GPT-3.5) While this technology is definitely entertaining, it’s not quite clear yet how it can effectively be applied to the needs of the typical enterprise. They can be fine-tuned on a smaller dataset to perform a specific task, such as language translation or summarization.

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Database AI AI Natural Language Processing 124

Chatbots Life

MAY 12, 2023

Such models can also learn from a set of few examples The process of presenting a few examples is also called In-Context Learning , and it has been demonstrated that the process behaves similarly to supervised learning. Either way, language models, like ChatGPT. Will ChatGPT replace your job?

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AI AI ML ML 90

Pickl AI

NOVEMBER 18, 2024

Key Takeaways Machine Learning Models are vital for modern technology applications. Types include supervised, unsupervised, and reinforcement learning. Key steps involve problem definition, data preparation, and algorithm selection. Ethical considerations are crucial in developing fair Machine Learning solutions.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Explosion

MAY 12, 2016

But I definitely think there’s still much more to come. SyntaxNet provides an important module in a natural language processing (NLP) pipeline such as spaCy. The thing that’s really significant is how quickly the speed and accuracy of natural language processing technologies is advancing. What’s next?

Natural Language Processing 40

Natural Language Processing Supervised Learning Algorithm 40

DagsHub

JANUARY 27, 2025

Unsupervised learning has shown a big potential in large language models but high-quality labelled data remains the gold standard for AI systems to be accurate and aligned with human language and understanding. Text labeling has enabled all sorts of frameworks and strategies in machine learning.

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing Supervised Learning 52

DagsHub

JANUARY 27, 2025

Unsupervised learning has shown a big potential in large language models but high-quality labelled data remains the gold standard for AI systems to be accurate and aligned with human language and understanding. Text labeling has enabled all sorts of frameworks and strategies in machine learning.

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing Supervised Learning 52

Pickl AI

MAY 31, 2023

Accordingly, there are many Python libraries which are open-source including Data Manipulation, Data Visualisation, Machine Learning, Natural Language Processing , Statistics and Mathematics. After that, move towards unsupervised learning methods like clustering and dimensionality reduction.

Data Science 52

Data Science Python Data Scientist Machine Learning 52

Snorkel AI

JANUARY 16, 2024

As humans, we learn a lot of general stuff through self-supervised learning by just experiencing the world. DK: I’m a big fan of the HELM (Holistic Evaluation of Language Models) project and Percy Liang’s work there and in general. Natural language processing itself shouldn’t just focus on text.

AI 52

AI AI Supervised Learning Algorithm 52

Snorkel AI

JANUARY 16, 2024

As humans, we learn a lot of general stuff through self-supervised learning by just experiencing the world. DK: I’m a big fan of the HELM (Holistic Evaluation of Language Models) project and Percy Liang’s work there and in general. Natural language processing itself shouldn’t just focus on text.

Supervised Learning 52

Supervised Learning AI AI Algorithm 52

Heartbeat

NOVEMBER 16, 2023

An In-depth Look into Evaluating AI Outputs, Custom Criteria, and the Integration of Constitutional Principles Photo by Markus Winkler on Unsplash Introduction In the age of conversational AI, chatbots, and advanced natural language processing, the need for systematic evaluation of language models has never been more pronounced.

AI 52

AI AI Deep Learning Deep Learning 52

Explosion

SEPTEMBER 17, 2013

Up-to-date knowledge about natural language processing is mostly locked away in academia. Averaged Perceptron POS tagging is a “supervised learning problem”. And it definitely doesn’t matter enough to adopt a slow and complicated algorithm like Conditional Random Fields. We’re careful. Here’s the problem.

Python 40

Python Algorithm Natural Language Processing Supervised Learning 40

Flipboard

APRIL 4, 2025

Large language models (LLMs) can be used to perform natural language processing (NLP) tasks ranging from simple dialogues and information retrieval tasks, to more complex reasoning tasks such as summarization and decision-making. This leads to responses that are untruthful, toxic, or simply not helpful to the user.

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AI AI Algorithm Artificial Intelligence 143

Dataconomy

MARCH 19, 2025

This deep learning model is designed to extract hierarchical representations from unlabeled data, setting a strong foundation for tasks across various domains, including image recognition and natural language processing. Here, labeled data comes into play as it is used for supervised learning.

Natural Language Processing 91

Natural Language Processing Supervised Learning Deep Learning Deep Learning 91

Dataconomy

MARCH 13, 2025

These sophisticated algorithms facilitate a deeper understanding of data, enabling applications from image recognition to natural language processing. What is deep learning? Deep learning is a subset of artificial intelligence that utilizes neural networks to process complex data and generate predictions.

Deep Learning 91

Deep Learning Deep Learning Natural Language Processing Machine Learning 91

Dataconomy

MARCH 6, 2025

Definition and characteristics of algorithms Algorithms are characterized by their systematic procedures. Machine learning as an algorithm example Machine learning encompasses a variety of algorithms that learn from data and improve over time. It represents a logical framework for approaching tasks and finding solutions.

Algorithm 91

Algorithm Machine Learning Machine Learning Deep Learning 91

AWS Machine Learning Blog

MARCH 27, 2025

In this post, we explore how you can use Amazon Bedrock to generate high-quality categorical ground truth data, which is crucial for training machine learning (ML) models in a cost-sensitive environment. When you evaluate a case, evaluate the definitions in order and label the case with the first definition that fits.

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AWS ETL ML ML 110