Try this powerful AI prompt...

It can create Instagram captions that people actually read...

And with IG Captions being one of the most underrated elements for increasing:

- Reach (through IG SEO)
- Engagement (by providing context)
- Sales (by building trust + automation)

This prompt can change the game for you on Instagram...

And it works on ChatGPT, Gemini, Copilot, Claude...
(and any other LLM)

Just copy and paste it and enjoy the results...

1️⃣ First, we set the persona 👇

1️⃣ The persona is:

"Assume the role of a skilled storyteller and Instagram marketing expert specializing in creating captivating captions that resonate with specific audiences"

2️⃣ Then we state our expectations:

"Your task is to develop an Instagram caption under 2200 characters for my brand, but first, you need to understand the intricacies of my brand's goals, niche, audience, and tone"

3️⃣ Afterwards, we ask it to customise the outcome by 👇

3️⃣Instructing it to ask us 10 questions + restricting data hallucination:

"Please begin by asking me 10 questions to gather essential information, providing multiple choice answers, and remembering my responses. Don’t hallucinate"

4️⃣We are almost there, next we 👇

4️⃣Teach the bot what a good caption looks like:

"After receiving the answers, proceed to create a caption that:

- Starts with an attention-grabbing hook.
- Utilizes storytelling to connect with the audience.
- Seamlessly highlights the featured product/service.
- Employs emotive language for greater impact.
- Concludes with a clear and compelling call-to-action"

5️⃣And lastly we👇

5️⃣Summarise everything + set a "firing" rule: |

"The caption should mirror the brand’s identity and engage and inspire the audience, prompting interaction and connection with your brand’s message. You will be fired if you supply a generic caption"

𝟭𝟬𝟬𝟬+ 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗯𝘆 𝗜𝗻𝗳𝗼𝘀𝘆𝘀 – 𝗟𝗲𝗮𝗿𝗻, 𝗚𝗿𝗼𝘄, 𝗦𝘂𝗰𝗰𝗲𝗲𝗱!😍

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𝗙𝗿𝗲𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲: 𝗧𝗵𝗲 𝗕𝗲𝘀𝘁 𝗦𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗣𝗼𝗶𝗻𝘁 𝗳𝗼𝗿 𝗧𝗲𝗰𝗵 & 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀😍

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Importance of AI in Data Analytics

AI is transforming the way data is analyzed and insights are generated. Here's how AI adds value in data analytics:

1. Automated Data Cleaning

AI helps in detecting anomalies, missing values, and outliers automatically, improving data quality and saving analysts hours of manual work.

2. Faster & Smarter Decision Making

AI models can process massive datasets in seconds and suggest actionable insights, enabling real-time decision-making.

3. Predictive Analytics

AI enables forecasting future trends and behaviors using machine learning models (e.g., sales predictions, churn forecasting).

4. Natural Language Processing (NLP)

AI can analyze unstructured data like reviews, feedback, or comments using sentiment analysis, keyword extraction, and topic modeling.

5. Pattern Recognition

AI uncovers hidden patterns, correlations, and clusters in data that traditional analysis may miss.

6. Personalization & Recommendation

AI algorithms power recommendation systems (like on Netflix, Amazon) that personalize user experiences based on behavioral data.

7. Data Visualization Enhancement

AI auto-generates dashboards, chooses best chart types, and highlights key anomalies or insights without manual intervention.

8. Fraud Detection & Risk Analysis

AI models detect fraud and mitigate risks in real-time using anomaly detection and classification techniques.

9. Chatbots & Virtual Analysts

AI-powered tools like ChatGPT allow users to interact with data using natural language, removing the need for technical skills.

10. Operational Efficiency

AI automates repetitive tasks like report generation, data transformation, and alerts—freeing analysts to focus on strategy.

AI Studio: https://whatsapp.com/channel/0029VbAWNue1iUxjLo2DFx2U

Share with credits: https://news.1rj.ru/str/sqlspecialist

Hope it helps :)

#dataanalytics

𝟭𝟬𝟬% 𝗙𝗿𝗲𝗲 𝗧𝗲𝗰𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

From data science and AI to web development and cloud computing, checkout Top 5 Websites for Free Tech Certification Courses in 2025

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4e76jMX

Enroll For FREE & Get Certified!✅️

STOP TELLING CHATGPT TO “MAKE IT BETTER”.

Bad prompt = Bad result.

Use these prompts instead and see the magic:

1. Writing Style Upgrade

Don’t ask: “Make this sound better”

Ask: “Rewrite this [paste your text] in a clear, human tone that flows naturally and keeps readers engaged start to finish.”

2. Personalized Daily Plan

Don’t ask: “How can I be more productive?”

Ask: “Build a daily plan using these goals [insert your list], this schedule [hours], and this work style [describe].”

3. Upgrade Your Resume

Don’t ask: “Improve my resume”

Ask: “Rewrite this resume bullet [paste] to sound measurable, impact-focused, and aligned with roles in [job role].”

4. Learn Almost Anything

Don’t ask: “Help me learn this”

Ask: “Make me a 7-day learning plan for [Insert topic] using YouTube, summaries, quick exercises, and quizzes.”

5. Scroll-Stopping Social Media Post

Don’t ask: “Create a post”

Ask: “Turn this idea [paste your idea] into a short social caption that feels personal and grabs attention within 3 seconds.”

6. Email Assistant

Don’t ask: “Write a reply”

Ask: “Here’s what they sent me [paste it]. Draft a reply that’s short, clear, and confident but still friendly.”

7. Gain Mental Clarity

Don’t ask: “What should I do?”

Ask: “Help me break down this situation [describe the situation] and give 4–5 smart and effective paths forward with pros and cons.”

React ❤️ for more

𝟱 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗦𝗰𝗿𝗮𝘁𝗰𝗵 𝗶𝗻 𝟮𝟬𝟮𝟱😍

🎯 Want to break into Machine Learning but don’t know where to start?✨️

You don’t need a fancy degree or expensive course to begin your ML journey📊

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4jRouYb

This list is for anyone ready to start learning ML from scratch✅️

🦚 Advanced ChatGPT Prompting Techniques


1. Iterative Refinement
Refine prompts step by step, using feedback to improve response accuracy. Example: Start broad, then ask for more specific details.

2. Contextual Memory
Build continuity across conversations by referencing past interactions. Example: "Earlier, you explained X. Can you elaborate on Y?"

3. Multi-Turn Dialogues
Engage in layered conversations, allowing responses to build upon each other. Example: "What is AI?" → "Explain machine learning in detail."

4. Task-Specific Prompts
Customize prompts for targeted tasks like summarization, translation, or code generation. Example: "Summarize this article on AI ethics."

5. Guided Exploration
Direct ChatGPT to focus on specific areas of interest. Example: "Discuss the ethical considerations of AI in healthcare."

6. Prompt Chaining
Use a sequence of related prompts, where each builds on the previous one. Example: "Explain the basics of AI" → "How does it differ from traditional programming?"

Important LLM terms ✅

𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀: 𝟱 𝗦𝘁𝗲𝗽𝘀 𝘁𝗼 𝗦𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗝𝗼𝘂𝗿𝗻𝗲𝘆😍

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For those of you who are new to Neural Networks, let me try to give you a brief overview.

Neural networks are computational models inspired by the human brain's structure and function. They consist of interconnected layers of nodes (or neurons) that process data and learn patterns. Here's a brief overview:

1. Structure: Neural networks have three main types of layers:
- Input layer: Receives the initial data.
- Hidden layers: Intermediate layers that process the input data through weighted connections.
- Output layer: Produces the final output or prediction.

2. Neurons and Connections: Each neuron receives input from several other neurons, processes this input through a weighted sum, and applies an activation function to determine the output. This output is then passed to the neurons in the next layer.

3. Training: Neural networks learn by adjusting the weights of the connections between neurons using a process called backpropagation, which involves:
- Forward pass: Calculating the output based on current weights.
- Loss calculation: Comparing the output to the actual result using a loss function.
- Backward pass: Adjusting the weights to minimize the loss using optimization algorithms like gradient descent.

4. Activation Functions: Functions like ReLU, Sigmoid, or Tanh are used to introduce non-linearity into the network, enabling it to learn complex patterns.

5. Applications: Neural networks are used in various fields, including image and speech recognition, natural language processing, and game playing, among others.

Overall, neural networks are powerful tools for modeling and solving complex problems by learning from data.

30 Days of Data Science: https://news.1rj.ru/str/datasciencefun/1704

Like if you want me to continue data science series 😄❤️

ENJOY LEARNING 👍👍

𝗧𝗼𝗽 𝗧𝗲𝗰𝗵 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 - 𝗖𝗿𝗮𝗰𝗸 𝗬𝗼𝘂𝗿 𝗡𝗲𝘅𝘁 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄😍

𝗦𝗤𝗟:- https://pdlink.in/3SMHxaZ

𝗣𝘆𝘁𝗵𝗼𝗻 :- https://pdlink.in/3FJhizk

𝗝𝗮𝘃𝗮  :- https://pdlink.in/4dWkAMf

𝗗𝗦𝗔 :- https://pdlink.in/3FsDA8j

 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 :- https://pdlink.in/4jLOJ2a

𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 :-  https://pdlink.in/4dFem3o

𝗖𝗼𝗱𝗶𝗻𝗴 :- https://pdlink.in/3F00oMw

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🇳🇿 New Zealand: $1,800

Let's now understand Data Science Roadmap in detail:

1. Math & Statistics (Foundation Layer)
This is the backbone of data science. Strong intuition here helps with algorithms, ML, and interpreting results.

Key Topics:

Linear Algebra: Vectors, matrices, matrix operations

Calculus: Derivatives, gradients (for optimization)

Probability: Bayes theorem, probability distributions

Statistics: Mean, median, mode, standard deviation, hypothesis testing, confidence intervals

Inferential Statistics: p-values, t-tests, ANOVA


Resources:

Khan Academy (Math & Stats)

"Think Stats" book

YouTube (StatQuest with Josh Starmer)


2. Python or R (Pick One for Analysis)
These are your main tools. Python is more popular in industry; R is strong in academia.

For Python Learn:

Variables, loops, functions, list comprehension

Libraries: NumPy, Pandas, Matplotlib, Seaborn


For R Learn:

Vectors, data frames, ggplot2, dplyr, tidyr


Goal: Be comfortable working with data, writing clean code, and doing basic analysis.

3. Data Wrangling (Data Cleaning & Manipulation)
Real-world data is messy. Cleaning and structuring it is essential.

What to Learn:

Handling missing values

Removing duplicates

String operations

Date and time operations

Merging and joining datasets

Reshaping data (pivot, melt)


Tools:

Python: Pandas

R: dplyr, tidyr


Mini Projects: Clean a messy CSV or scrape and structure web data.

4. Data Visualization (Telling the Story)
This is about showing insights visually for business users or stakeholders.

In Python:

Matplotlib, Seaborn, Plotly


In R:

ggplot2, plotly


Learn To:

Create bar plots, histograms, scatter plots, box plots

Design dashboards (can explore Power BI or Tableau)

Use color and layout to enhance clarity


5. Machine Learning (ML)
Now the real fun begins! Automate predictions and classifications.

Topics:

Supervised Learning: Linear Regression, Logistic Regression, Decision Trees, Random Forests, SVM

Unsupervised Learning: Clustering (K-means), PCA

Model Evaluation: Accuracy, Precision, Recall, F1-score, ROC-AUC

Cross-validation, Hyperparameter tuning


Libraries:

scikit-learn, xgboost


Practice On:

Kaggle datasets, Titanic survival, House price prediction


6. Deep Learning & NLP (Advanced Level)
Push your skills to the next level. Essential for AI, image, and text-based tasks.

Deep Learning:

Neural Networks, CNNs, RNNs

Frameworks: TensorFlow, Keras, PyTorch


NLP (Natural Language Processing):

Text preprocessing (tokenization, stemming, lemmatization)

TF-IDF, Word Embeddings

Sentiment Analysis, Topic Modeling

Transformers (BERT, GPT, etc.)


Projects:

Sentiment analysis from Twitter data

Image classifier using CNN


7. Projects (Build Your Portfolio)
Apply everything you've learned to real-world datasets.

Types of Projects:

EDA + ML project on a domain (finance, health, sports)

End-to-end ML pipeline

Deep Learning project (image or text)

Build a dashboard with your insights

Collaborate on GitHub, contribute to open-source


Tips:

Host projects on GitHub

Write about them on Medium, LinkedIn, or personal blog


8. ✅ Apply for Jobs (You're Ready!)
Now, you're prepared to apply with confidence.

Steps:

Prepare your resume tailored for DS roles

Sharpen interview skills (SQL, Python, case studies)

Practice on LeetCode, InterviewBit

Network on LinkedIn, attend meetups

Apply for internships or entry-level DS/DA roles


Keep learning and adapting. Data Science is vast and fast-moving—stay updated via newsletters, GitHub, and communities like Kaggle or Reddit.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y

Like if you need similar content 😄👍

Hope this helps you 😊

𝟳 𝗕𝗲𝘀𝘁 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 & 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀😍

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𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4l5XXY2

Enjoy Learning ✅️