Data Science Blog

FEBRUARY 23, 2023

My point is, the more data you have, and the bigger computation resource you have, the better performance you get. In other words, machine learning has scalability with data and parameters. This characteristic is clearly observed in models in natural language processing (NLP) and computer vision (CV) like in the graphs below.

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Supervised Learning Machine Learning Machine Learning Deep Learning 264

Pickl AI

JULY 12, 2024

Neural networks are inspired by the structure of the human brain, and they are able to learn complex patterns in data. Deep Learning has been used to achieve state-of-the-art results in a variety of tasks, including image recognition, Natural Language Processing, and speech recognition.

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Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

AWS Machine Learning Blog

AUGUST 12, 2024

You can also tap into the power of automated machine learning (AutoML) and automatically build custom ML models for regression, classification, time series forecasting, natural language processing, and computer vision, supported by Amazon SageMaker Autopilot.

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ML ML AWS AI 125

Heartbeat

JANUARY 30, 2023

Source:datascientist.com Sentiment analysis, commonly referred to as “opinion mining,” is the method of drawing out irrational information from written or spoken words. The study of how people communicate their thoughts, beliefs, and feelings through language is a fast-expanding area of natural language processing (NLP).

EDA 52

EDA Machine Learning Machine Learning Exploratory Data Analysis 52

Pickl AI

JULY 20, 2023

Prescriptive Analytics Projects: Prescriptive analytics takes predictive analysis a step further by recommending actions to optimize future outcomes. NLP techniques help extract insights, sentiment analysis, and topic modeling from text data. 6. Analyzing Large Datasets: Choose a large dataset from public sources (e.g.,

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Analytics Analytics Big Data Big Data 40

Pickl AI

DECEMBER 16, 2024

Long Short-Term Memory (LSTM) A type of recurrent neural network (RNN) designed to learn long-term dependencies in sequential data. Facebook Prophet A user-friendly tool that automatically detects seasonality and trends in time series data. Making Data Stationary: Many forecasting models assume stationarity.

AI 52

AI AI Machine Learning Machine Learning 52

Heartbeat

MAY 29, 2023

LLMs are one of the most exciting advancements in natural language processing (NLP). We will explore how to better understand the data that these models are trained on, and how to evaluate and optimize them for real-world use.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

AWS Machine Learning Blog

JUNE 5, 2025

Legacy workflow: On-premises ML development and deployment When the data science team needed to build a new fraud detection model, the development process typically took 24 weeks. About the Authors Jake Wen is a Solutions Architect at AWS, driven by a passion for Machine Learning, Natural Language Processing, and Deep Learning.

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ODSC - Open Data Science

JULY 19, 2023

Model Development (Inner Loop): The inner loop element consists of your iterative data science workflow. A typical workflow is illustrated here from data ingestion, EDA (Exploratory Data Analysis), experimentation, model development and evaluation, to the registration of a candidate model for production.

Machine Learning 52

Machine Learning Machine Learning Azure Data Science 52

Pickl AI

SEPTEMBER 12, 2024

Deep Learning : A subset of Machine Learning that uses Artificial Neural Networks with multiple hidden layers to learn from complex, high-dimensional data. Exploratory Data Analysis (EDA): Analysing and visualising data to discover patterns, identify anomalies, and test hypotheses.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

DrivenData Labs

DECEMBER 7, 2023

His main research interests revolve around applications of Network Analysis and Natural Language Processing methods. Artem has versatile experience in working with real-life data from different domains and was involved in several data science projects at the World Bank and the University of Oxford.

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