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Evolving Trends in Data Science: Insights from ODSC Conference Sessions from 2015 to 2024

ODSC - Open Data Science

Tools like Python , R , and SQL were mainstays, with sessions centered around data wrangling, business intelligence, and the growing role of data scientists in decision-making. By 2017, deep learning began to make waves, driven by breakthroughs in neural networks and the release of frameworks like TensorFlow.

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Leveraging user-generated social media content with text-mining examples

IBM Journey to AI blog

As it pertains to social media data, text mining algorithms (and by extension, text analysis) allow businesses to extract, analyze and interpret linguistic data from comments, posts, customer reviews and other text on social media platforms and leverage those data sources to improve products, services and processes. How does text mining work?

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Solve forecasting challenges for the retail and CPG industry using Amazon SageMaker Canvas

AWS Machine Learning Blog

The algorithms are: Convolutional Neural Network Quantile Regression (CNN-QR), DeepAR+ , Prophet , Non-Parametric Time Series (NPTS), Autoregressive Integrated Moving Average (ARIMA), and Exponential Smoothing (ETS).

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Your Ultimate Guide to Coursera Machine Learning Top Courses

How to Learn Machine Learning

Course Breakdown: Neural Networks and Deep Learning: Understand the foundations of deep learning Improving Deep Neural Networks: Master techniques for optimizing neural network performance Structured Machine Learning Projects: Learn best practices for machine learning project strategy Convolutional Neural Networks: Dive into computer vision and image (..)

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Artificial Neural Network: A Comprehensive Guide

Pickl AI

For example, convolutional neural networks (CNNs), a specific type of ANN, are widely used for image classification tasks, enabling applications such as facial recognition and object detection. They may employ neural networks to enhance predictive analytics and improve business outcomes.