Pickl AI

NOVEMBER 18, 2024

Summary: The blog provides a comprehensive overview of Machine Learning Models, emphasising their significance in modern technology. It covers types of Machine Learning, key concepts, and essential steps for building effective models. Key Takeaways Machine Learning Models are vital for modern technology applications.

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Dataconomy

MARCH 27, 2023

Some of the ways in which ML can be used in process automation include the following: Predictive analytics:  ML algorithms can be used to predict future outcomes based on historical data, enabling organizations to make better decisions. How can RPA improve data quality and streamline data management processes?

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Heartbeat

AUGUST 29, 2023

Deep learning is a branch of machine learning that makes use of neural networks with numerous layers to discover intricate data patterns. Deep learning models use artificial neural networks to learn from data. The training data is labeled.

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Pickl AI

AUGUST 1, 2024

Unlike traditional Machine Learning, which often relies on feature extraction and simpler models, Deep Learning utilises multi-layered neural networks to automatically learn features from raw data. Insufficient or low-quality data can lead to poor model performance and overfitting.

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Pickl AI

NOVEMBER 28, 2024

These techniques span different types of learning and provide powerful tools to solve complex real-world problems. Supervised Learning Supervised learning is one of the most common types of Machine Learning, where the algorithm is trained using labelled data.

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ODSC - Open Data Science

OCTOBER 15, 2023

Ikigai Labs Ikigai Labs is a company that provides a platform for building and managing natural language processing models. Outerbounds’ platform is valuable for businesses that want to improve their data quality and identify potential problems early on.

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AssemblyAI

SEPTEMBER 18, 2023

Original natural language processing (NLP) models were limited in their understanding of language. While LLMs offer potential advantages in terms of scalability and cost-efficiency, they also present meaningful challenges, especially concerning data quality, biases, and ethical considerations.

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Pickl AI

SEPTEMBER 12, 2024

Key Components of Data Science Data Science consists of several key components that work together to extract meaningful insights from data: Data Collection: This involves gathering relevant data from various sources, such as databases, APIs, and web scraping.

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DagsHub

NOVEMBER 11, 2024

Social media conversations, comments, customer reviews, and image data are unstructured in nature and hold valuable insights, many of which are still being uncovered through advanced techniques like Natural Language Processing (NLP) and machine learning. Tools like Unstructured.io

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DagsHub

JANUARY 27, 2025

A more formal definition of text labeling, also known as text annotation, would be the process of adding meaningful tags or labels to raw text to make it usable for machine learning and natural language processing tasks. Text labeling has enabled all sorts of frameworks and strategies in machine learning.

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The MLOps Blog

MARCH 21, 2023

Olalekan said that most of the random people they talked to initially wanted a platform to handle data quality better, but after the survey, he found out that this was the fifth most crucial need. And when the platform automates the entire process, it’ll likely produce and deploy a bad-quality model.

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phData

OCTOBER 21, 2024

What Are Large Language Models? Large Language Models are deep learning models that recognize, comprehend, and generate text, performing various other natural language processing (NLP) tasks. At its core, machine learning is about finding and learning patterns in data that can be used to make decisions.

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Dataconomy

MARCH 26, 2025

In an age dominated by data, understanding the intricacies of how this labeling works is essential for anyone looking to leverage AI technologies. What is AI data labeling? AI data labeling refers to the process of identifying and tagging data to train supervised learning models effectively.

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