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Legendary actor and mental health advocate Glenn Close is on a quest to change how we think about mental health, starting with her decision to speak out about her own family's struggles — a brave choice considering the stigma that pervades the topic. This week, we're revisiting this sweeping conversation with TEDWomen curator Pat Mitchell, where Close shares the inspiration behind the advocacy group she founded to combat the crisis, underscoring the transformative power of community and the critical need for comprehensive mental health care systems. Want to help shape TED’s shows going forward? Fill out our survey ! Become a TED Member today at https://ted.com/join Hosted on Acast. See acast.com/privacy for more information.…
Content provided by Lakshmikanth and Prashanth. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Lakshmikanth and Prashanth or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
Content provided by Lakshmikanth and Prashanth. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Lakshmikanth and Prashanth or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
The minimal difference that helps you understand what Data Scientist and an ML engineer would do in their day-to-day work. Data Science is the generic term, that fuses data and science in a right proportion. Data Science is the art of dealing with problem-related to contextual data which may be structured or unstructured. Fortune companies started consuming actionable insights from their data lake and so big data comes into the picture and the opportunities to handle those data are challenging. Data Scientist deals with data engineering, statistical modeling, and convey their findings through beautiful charts. Wherein ML engineers are responsible for delivering the outcomes of the model predictions and the engineering behind them. They write services to encapsulate the models, provide security for them, take care of deployment and if required optimize their performance and so. That's how data scientists and ML engineers differ in their work in nature.…
Social Data is an essential piece of your business marketing strategy. Social channels help you connect with your customers, increase awareness about your brand and boost your leads and sales. Let’s get some insights about social Data and its users adoption.
Datalicious is an exclusive data science podcast designed for data science enthusiasts
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