🤖 PromptMakr

🤖 : AI | art | image | prompts
🎨 : English

💬 The platform for Prompt Engineers to generate and share unlimited AI art prompts for free.

𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜? 𝗧𝗵𝗶𝘀 𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁 𝗶𝘀 𝗬𝗼𝘂𝗿 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗦𝗵𝗼𝗿𝘁𝗰𝘂𝘁!😍

Mastering Power BI can be overwhelming, but this cheat sheet by DataCamp makes it super easy! 🚀

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4ld6F7Y

No more flipping through tabs & tutorials—just pin this cheat sheet and analyze data like a pro!✅️

Don't waste your lot of time when learning data analysis.

Here's how you may start your Data analysis journey

1️⃣ - Avoid learning a programming language (e.g., SQL, R, or Python) for as long as possible.

This advice might seem strange coming from a former software engineer, so let me explain.

The vast majority of data analyses conducted each day worldwide are performed in the "solo analyst" scenario.

In this scenario, nobody cares about how the analysis was completed.

Only the results matter.

Also, the analysis methods (e.g., code) are rarely shared in this scenario.

Like for next steps

#dataanalysis

𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

Master Python, Machine Learning, SQL, and Data Visualization with hands-on tutorials & real-world datasets? 🎯

This 100% FREE resource from Kaggle will help you build job-ready skills—no fluff, no fees, just pure learning!

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3XYAnDy

Perfect for Beginners ✅️

SQL is one of the core languages used in data science, powering everything from quick data retrieval to complex deep dive analysis. Whether you're a seasoned data scientist or just starting out, mastering SQL can boost your ability to analyze data, create robust pipelines, and deliver actionable insights.

Let’s dive into a comprehensive guide on SQL for Data Science!

I have broken it down into three key sections to help you:

𝟭. 𝗦𝗤𝗟 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:
Get a handle on the essentials -> SELECT statements, filtering, aggregations, joins, window functions, and more.

𝟮. 𝗦𝗤𝗟 𝗶𝗻 𝗗𝗮𝘆-𝘁𝗼-𝗗𝗮𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:
See how SQL fits into the daily data science workflow. From quick data queries and deep-dive analysis to building pipelines and dashboards, SQL is really useful for data scientists, especially for product data scientists.

𝟯. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀:
Learn what interviewers look for in terms of technical skills, design and engineering expertise, communication abilities, and the importance of speed and accuracy.

𝗧𝗼𝗽 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝘃𝗶𝗿𝘁𝘂𝗮𝗹 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝘀😍

Want to work on real industry tasks, develop in-demand skills, and boost your resume—all for FREE? 

 Your dream career starts with real experience—grab this opportunity today!

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4bCyUIM

💡 No experience required—just learn, upskill & build your portfolio! 🚀

𝟱 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

Whether you’re a complete beginner or looking to level up, these courses cover Excel, Power BI, Data Science, and Real-World Analytics Projects to make you job-ready.

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3DPkrga

All The Best 🎊

𝟱 𝗙𝗿𝗲𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗹𝗮𝗻𝘀 𝘁𝗼 𝗨𝗽𝘀𝗸𝗶𝗹𝗹 𝗶𝗻 𝗧𝗲𝗰𝗵 & 𝗔𝗜!😍

Looking to boost your tech career?🚀

These free learning plans will help you stay ahead in DevOps, AI, Cloud Security, Data Analytics, and Machine Learning!📊

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4ijtDI2

Perfect for Beginners & Professionals Looking to Upskill!✅️

Three different learning styles in machine learning algorithms:

1. Supervised Learning

Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.

A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.

Example problems are classification and regression.

Example algorithms include: Logistic Regression and the Back Propagation Neural Network.

2. Unsupervised Learning

Input data is not labeled and does not have a known result.

A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.

Example problems are clustering, dimensionality reduction and association rule learning.

Example algorithms include: the Apriori algorithm and K-Means.

3. Semi-Supervised Learning

Input data is a mixture of labeled and unlabelled examples.

There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.

Example problems are classification and regression.

Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.

🎓 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗿𝗼𝗺 𝗢𝗽𝗲𝗻 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 – 𝗟𝗲𝗮𝗿𝗻, 𝗚𝗿𝗼𝘄 & 𝗨𝗽𝘀𝗸𝗶𝗹𝗹!😍

If you’re just starting your learning journey or looking to level up your skills—this is your golden opportunity! 🌟

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4cuo73X

⏳ Don’t miss out—bookmark this for later!