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10 Datasets by INDIAai for your Next Data Science Project

Analytics Vidhya

Per Statista, The Artificial Intelligence market in India is projected to grow by 28.63% (2024-2030), resulting in a market volume of US$28.36bn in 2030. It is visible that AI is booming, […] The post 10 Datasets by INDIAai for your Next Data Science Project appeared first on Analytics Vidhya.

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Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

The market for data warehouses is booming. billion by 2030. While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. We talked about enterprise data warehouses in the past, so let’s contrast them with data lakes. Data Warehouse.

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Digital Twin Use Races Ahead at McLaren Group

CIO Business Intelligence

billion by 2030. Like professional basketball, industrial-scale farming, national politics, and global merchandising, auto racing has become a data science. Modern data analytics spans a range of technologies, from dedicated analytics platforms and databases to deep learning and artificial intelligence (AI).

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How to choose the best AI platform

IBM Big Data Hub

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually.

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Five machine learning types to know

IBM Big Data Hub

What is machine learning? ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions. Manage a range of machine learning models with watstonx.ai

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Getting ready for artificial general intelligence with examples

IBM Big Data Hub

Regardless, given the wide range of predictions for AGI’s arrival, anywhere from 2030 to 2050 and beyond, it’s crucial to manage expectations and begin by using the value of current AI applications. Building an in-house team with AI, deep learning , machine learning (ML) and data science skills is a strategic move.