Mr R

[Phosphen @phosphenq]

https://t.co/XtP3Q0sQoc

[Mr R @MrRevol3]

https://t.co/zNPM625ERP

[Hikari.Cult🩸🩸🩸 🍉 🎭🐉🍄🎣🦦☮️🤍🍓🎇🐈🐕🌕🌒33 @hikari_oshi]

🧵 1/8
1. The @RyoshiResearch & @MrOmodulus connection & a BIT more 👀😉
Okay here it is $Cult & $Shib Army.
Ever wondered why Ryoshi had a dog, & how a name like Mr OModulus who wears a mask could be connected, is it random? Well it's NOT Random.Buckle up you are in for a ride🎢

[Sean Kelly @seanpk]

I take more than 40 supplements a day.

At 41, my biological age is just 27.

If this picture makes you cringe, you need to understand the science behind why it works.

A thread 🧵

[Alex Park | Think Brighter Newsletter @AlexThinkBright]

2.

[Swapna Kumar Panda @swapnakpanda]

Looking for FREE Data Science Certificates?

❯ Python
https://t.co/PabSXf4T0o

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❯ Machine Learning
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❯ Deep Learning
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[Shushant Lakhyani @shushant_l]

50 YouTube channels to accelerate your learning:

1. Periodic Videos: Chemistry videos
2. Veritasium: Science and engineering
3. Langfocus: Language and linguistics
4. YaleCourses: Yale University courses
5. History Buffs: Historical film accuracy
6. ChefSteps: Culinary skills and recipes
7. Adam Neely: Music theory and culture
8. Lindsay Ellis: Film theory and criticism
9. Yoga With Adriene: Yoga and wellness
10. 3Blue1Brown: Complex math, visualized
11. LegalEagle: Legal analysis and insights
12. AsapSCIENCE: Quick, fun science facts
13. Numberphile: Engaging math discussions
14. Art Assignment: Art history and projects
15. The Futur: Business and design education
16. CrashCourse: Diverse subjects in a nutshell
17. Primer: Economics and business concepts
18. CodeBullet: Coding challenges and games
19. Dr. Becky: Astrophysics and space science
20. Jacob Collier: Music innovation and theory
21. Computerphile: Computer science concepts
22. Bright Side: Fun facts and creative thinking
23. TED-Ed: Educational animations and lessons
24. Tom Scott: Amazing places and technology
25. MIT OpenCourseWare: College-level courses
26. The School of Life: Philosophy and life skills
27. Stanford Online: Stanford University lectures
28. BBC Learning English: English language skills
29. National Geographic: Nature and anthropology
30. MinutePhysics: Physics concepts in short clips
31. Physics Girl: Physics concepts and experiments
32. Draw with Jazza: Art and animation techniques
33. TierZoo: Wildlife and nature in a gaming context
34. MindYourDecisions: Puzzles and math problems
35. Vsauce: Deep dives into science and philosophy
36. Everyday Astronaut: Space and rocket education
37. The History Guy: Lesser-known historical events
38. Geography Now: Country-by-country geography
39. The Coding Train: Coding and creative computing
40. PatrickJMT: Math tutorials from basic to advanced
41. Sixty Symbols: Physics and astronomy discussions
42. Khan Academy: Comprehensive educational resource
43. SmarterEveryDay: Science application in everyday life
44. Bozeman Science: Comprehensive science education
45. MoneySavingExpert: Personal finance and saving tips
46. SciShow: Curiosities and exciting scientific phenomena
47. The Organic Chemistry Tutor: Math and science tutorials
48. Charisma on Command: Interpersonal skills and charisma
49. Kurzgesagt – In a Nutshell: Complex topics in simple terms
50. MedCram - Medical Lectures: Medical knowledge and health

---

Get trending AI updates, tools, and tutorials: https://t.co/o1TXMQrkZv

---

[Perseus @PerseusLeGrand]

Dans la section "Œil", vous noterez tout ce que vous avez remarqué d'intéressant au cours de la journée.

- Une belle chose
- Quelque chose qui sort de l'ordinaire
- Un élément qui vous a fasciné, qui vous a plu, déplu...

Tout ce que vous voulez, tant que cela vous intéresse.

Dans la section "Main", vous noterez ce que vous avez fait au cours de la journée, ce que vous avez accompli.

Dans la section "Oreille", vous allez écrire ce que vous avez pu entendre d’intéressant, ce que vous avez écouter, les petits éléments que vous avez pu saisir en tendant l’oreille, etc.

- Une information pertinente
- Une anecdote
- Une histoire...

Et enfin, en bas à droite, vous écrirez une phrase qui résume votre journée. Mais vous veillerez à l'écrire de la plus belle des manières !

Simple, efficace.

[Arsalan @AIwithArsalan]

I Don't Understand Why People Chase After 9-to-5 Grind!

Here Are 7 Website That Pay You Daily

No Skill or Experience Required:

[Modest Mitkus @ModestMitkus]

Me in 2021: making $400/month.
Me in 2024: making $30,000/month.

Here are 6 high-income skills that made it possible:

[SAROJ PARAJULI @Parajulisaroj16]

Spatial statistics is the branch of statistics that deals with data distributed in space or across geographic locations. https://t.co/qIc8WujfbD
#DataScience #rstats #DataAnalytics #DataScientist #statistics #maths #r #programming #dataviz #codinglife

[Sasi 📊📈 @freest_man]

Excel for Data Science Complete Study Plan 🚀

The timeline of 30 days and you have to dedicate at least 1 hour a day.

Week 1: Basics of Excel

Day 1-2: Excel Basics

https://t.co/ecWfpJw9dl

Day 3-4: Understanding Formulas, Functions and Formatting

https://t.co/LfcuAQ1ZDX

Day 5-7: Practice Basic Operations

Practice: Start by working on simple datasets, performing basic arithmetic operations, and using functions.

Datasets: https://t.co/p8NHqsiW1k

Week 2: Data Manipulation

Day 8-10: Sorting, Filtering, and Data Validation

Sorting and Filtering: https://t.co/ThWE4m5WfF

Data Validation: https://t.co/HkbEebW8kI

Day 11-12: PivotTables

Resource: https://t.co/PymxBY2xYp

Day 13-14: Practice Data Manipulation

Practice: Work with moderately complex datasets, practice sorting, filtering, and creating PivotTables.

Example: https://t.co/RwLbSwNHnq

Week 3: Advanced Functions and Analysis

Day 15-20: Advanced Functions

Resource: https://t.co/uRiRZH0auj

Day 21-22: Statistical Functions and Analysis

Resource: https://t.co/wfksZfWvBV

Week 4: Visualization and Presentation

Day 23-25: Charts and Graphs

https://t.co/ARX3xFfx9a

Day 26-28: Advanced Visualizationhttps://www.youtube.com/watch?v=HrqgBhZDWUg&list=PLDO6p2ilGI3EkDJZtQqi3VUdnIhW6lQUu

Day 29-30: Final Projects and RecapPractice: Apply learned skills to create comprehensive visual presentations using Excel.

Example Project: https://t.co/g9EX4w92gI

Datasets: https://t.co/p8NHqsiW1k

Throughout the Month:

Review and Practice: Regularly revisit previous topics, practice, and work on sample datasets to reinforce your learning.

[د. عمر الرهبيني | Dr. Omar AlRahbeeni @Orahbeeni]

الآن الدمج الضارب🔥🔥🔥🤯🤯🚨🔴
أداة تدمج لك أقوى برنامجين على وجه الارض
(EXCEL + ChatGPT) حدثني عن القوة🤯😍

باشرحها واحط تفاصيل عنها وكيف تستعملها تجت التغريدة، تابع:🌟

[Documenting ₿itcoin 📄 @DocumentingBTC]

Best photos of #bitcoin history - thread 🧵

[Sasi 📊📈 @freest_man]

KPIs are an important part of your business

But choosing your KPIs correctly is even more important

Be SMART about your KPIs

1/ Specific

Specific KPIs are useful in measuring accurately

It should be clearly defined and easy to track them

Example: Number of outbound cold emails sent per day

2/ Measurable

Define what's being measured correctly

The value can be a numerical value or a percentage

Example: Cold email open rate increased by 40%

3/ Attainable

The KPI should be attainable

It should inspire your employees rather than discouraged

Example: 7% Cold email response rate

4/ Relevant

The KPIs should be aligned to the business both long and short term

It should very important to the business's success

Example - Increase in conversion rate by 20% by the end of the month

5/ Time-Bound

Time frames are important for achieving targets for a business

Easier to track progress and helps to prioritize on goals

Example - Increase the qualified leads by the end of the quarter by 25%

--

Liked the content? Leave a ♥️

Follow for more!

[Mr R @MrRevol3]

True 👌

[Matt Dancho (Business Science) @mdancho84]

Stop learning data science.

Start learning how to solve problems. 🧵

#datascience #rstats #stats

[Akshay 🚀 @akshay_pachaar]

Precision, Recall clearly explained:

[Akshay 🚀 @akshay_pachaar]

This should be impossible!

In just 3 lines of code, this open-source library can clean any ML dataset!

You can:
- flag outliers
- find label errors
- identify near duplicates
- perform active-learning
- find out of distribution samples
- And more ...

Cleanlab is a Confident Learning based Python library that works with:

- Any data ( text, image, tabular, audio, etc. )
- Any ML tasks ( classification, tagging, entity recognition, prompting LLMs & more ... )

Check this out👇

You must be wondering how it works❗️💭🤔

Cleanlab is developed by folks at MIT, and it's powered by a novel algorithm called Confident Learning!

Below, I have provided a simplified explanation of the same!

Check this out👇

Cleanlab also has a no-code studio that let's you clean any data & train robust models in just a few clicks!

I have shared all the relevant links in the next tweet!

That's a wrap, thanks for reading!

Find me → @akshay_pachaar ✔️
For more content like this!

Cheers! 🥂

[Sasi 📊📈 @freest_man]

How to create engaging Dashboards?

Let me show you