You have to create a Chatbot for ECS. Whoever wants to know about ECS, they can learn from this chatbot.
What is Amazon Elastic Container Service? - Amazon Elastic Container Service
Amazon Elastic Container Service
FS1253 : Handwritten Testimonials
official names: ClassicTesti
FS1254: Find Marketing Content
The 3.0 release of JupyterLab brings many new features! Like a visual debugger
$ 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚓𝚞𝚙𝚢𝚝𝚎𝚛𝚕𝚊𝚋==3
Features • Stepping into Python code in JupyterLab with the visual debugger • The table of contents extension now ships with JupyterLab. This makes it easy to see and navigate the structure of a document • Support for multiple display languages
And many more check it here
⚡ Spread the Open Source love If you know an amazing project, paper or library drop me a message here on LinkedIn or Twitter @philipvollet
In the above content, find the marketing content and highlight them by using NLP.
Show a table/visual for various library versions and their release date
FS1256: Google Form ++
It's hard to view the Google Form without navigation. If you could improve the viewing option by Left, Right Nav it would be great. You will have to read the Excel sheet in Python and show the viewing page
FS1257: Open Source Contribution per capita
We need to find how many open source contributors in South India and Ontario and compare them regularly.
We need to increate the open source contributors count in South India. This is the ultimate goal of this project.
FS1258: California Exodus
Do some visual about this topic.
Make a visual as this image.
Do some ground work on TinkerBell and write an article about it.
Encoption - TBD
LibAlerts - TBD
Be careful out there: LinkedIn is infested with fake profiles. Out of thousands of invites I've received, hundreds have been fake. How can you recognize a fake profile?
Fakers use patterns and make mistakes that give them out. Here are some examples:
You can't find the same person elsewhere with a simple Google search.
Their work and education history is from large institutions with no unique details.
The name and professional summary are generic.
Participated groups and pages don't show a unique history.
Historical participation on posts doesn't exist or doesn't fit the background or location.
The friend list doesn't fit the background.
With Google image search the picture is elsewhere with another name. This is less useful today because it is so easy to generate deepfake images.
The image below shows a fake profile that has gathered 413 connections, many of them Finns. The name is generic, lacking surname. The photo looks like an image bank model or a deepfake. The study program she claims to participate doesn't exist.
Have you recognized fake profiles among your invites yet? Any other ways you have used to recognize a fake profile?
sample fake profile
GitHub - naiveHobo/InvoiceNet: Deep neural network to extract intelligent information from invoice documents.
Do some ground work on this library and show a proof.
FS1265: AWS Invoice Analyzer
Analyze AWS Invoices and recommend us
FS1266: Robinhood Graph Clone
FS1267: Red/Green Flags
Analyze these documents by using ML algorithms and show me only red/green flags. Highlight the important things as points not paragraphs.
Keep a lingo like urban dictionary but only for you and your friends circle. Keep Login to keep it private and share with your friends.