Prompt Engineering in itself does not warrant a separate job.
Most of the things you see online related to prompts (especially things said by people selling courses) is mostly just writing some crazy text to get ChatGPT to do some specific task. Most of these prompts are just been found by serendipity and are never used in any company. They may be fine for personal usage but no company is going to pay a person to try out prompts 😅. Also a lot of these prompts don't work for any other LLMs apart from ChatGPT.
You have mostly two types of jobs in this field nowadays, one is more focused on training, optimizing and deploying models. For this knowing the architecture of LLMs is critical and a strong background in PyTorch, Jax and HuggingFace is required. Other engineering skills like System Design and building APIs is also important for some jobs. This is the work you would find in companies like OpenAI, Anthropic, Cohere etc.
The other is jobs where you build applications using LLMs (this comprises of majority of the companies that do LLM related work nowadays, both product based and service based). Roles in these companies are called Applied NLP Engineer or ML Engineer, sometimes even Data Scientist roles. For this you mostly need to understand how LLMs can be used for different applications as well as know the necessary frameworks for building LLM applications (Langchain/LlamaIndex/Haystack). Apart from this, you need to know LLM specific techniques for applications like Vector Search, RAG, Structured Text Generation. This is also where some part of your role involves prompt engineering. Its not the most crucial bit, but it is important in some cases, especially when you are limited in the other techniques.
Most of the things you see online related to prompts (especially things said by people selling courses) is mostly just writing some crazy text to get ChatGPT to do some specific task. Most of these prompts are just been found by serendipity and are never used in any company. They may be fine for personal usage but no company is going to pay a person to try out prompts 😅. Also a lot of these prompts don't work for any other LLMs apart from ChatGPT.
You have mostly two types of jobs in this field nowadays, one is more focused on training, optimizing and deploying models. For this knowing the architecture of LLMs is critical and a strong background in PyTorch, Jax and HuggingFace is required. Other engineering skills like System Design and building APIs is also important for some jobs. This is the work you would find in companies like OpenAI, Anthropic, Cohere etc.
The other is jobs where you build applications using LLMs (this comprises of majority of the companies that do LLM related work nowadays, both product based and service based). Roles in these companies are called Applied NLP Engineer or ML Engineer, sometimes even Data Scientist roles. For this you mostly need to understand how LLMs can be used for different applications as well as know the necessary frameworks for building LLM applications (Langchain/LlamaIndex/Haystack). Apart from this, you need to know LLM specific techniques for applications like Vector Search, RAG, Structured Text Generation. This is also where some part of your role involves prompt engineering. Its not the most crucial bit, but it is important in some cases, especially when you are limited in the other techniques.
❤5👍3
🚀 What is an AI Agent?
An AI Agent is a smart software system that perceives its environment, makes decisions, and takes actions—all on its own, with minimal human help. Think of it like a digital assistant that doesn’t just wait for instructions, but actually figures out what to do next and gets things done for you.
Key Abilities of AI Agents:
1. Autonomy: Acts independently, choosing the best actions to reach a goal.
2. Goal-Oriented: Always working towards specific outcomes, whether it’s booking a meeting or sorting emails.
3. Adaptability: Learns from new data and changes its approach as things shift—just like a human would.
4. Reasoning: Weighs options, solves problems, and makes decisions based on logic and data.
5. Learning: Gets smarter over time by analyzing past results and improving its methods.
How Do AI Agents Work?
- They *sense* their environment (like reading emails or listening to your voice).
- They *analyze* what’s happening using AI tools like natural language processing and machine learning.
- They decide the next steps, sometimes even creating subtasks or calling external tools if needed.
- They act—whether it’s sending an email, booking a cab, or summarizing a report.
Real-World Examples:
- Virtual assistants (like Siri or Alexa) that manage your schedule.
- Chatbots handling customer support.
- Self-driving cars navigating traffic.
- AI tools automating business workflows or IT tasks.
Why Are AI Agents a Big Deal?
They free up your time by handling repetitive or complex tasks, work 24/7, adapt to your needs, and can even collaborate with other agents to tackle bigger challenges.
In short: AI Agents are your digital teammates—always learning, always working, and always aiming to make your life easier! 😎
React ♥️ for more
An AI Agent is a smart software system that perceives its environment, makes decisions, and takes actions—all on its own, with minimal human help. Think of it like a digital assistant that doesn’t just wait for instructions, but actually figures out what to do next and gets things done for you.
Key Abilities of AI Agents:
1. Autonomy: Acts independently, choosing the best actions to reach a goal.
2. Goal-Oriented: Always working towards specific outcomes, whether it’s booking a meeting or sorting emails.
3. Adaptability: Learns from new data and changes its approach as things shift—just like a human would.
4. Reasoning: Weighs options, solves problems, and makes decisions based on logic and data.
5. Learning: Gets smarter over time by analyzing past results and improving its methods.
How Do AI Agents Work?
- They *sense* their environment (like reading emails or listening to your voice).
- They *analyze* what’s happening using AI tools like natural language processing and machine learning.
- They decide the next steps, sometimes even creating subtasks or calling external tools if needed.
- They act—whether it’s sending an email, booking a cab, or summarizing a report.
Real-World Examples:
- Virtual assistants (like Siri or Alexa) that manage your schedule.
- Chatbots handling customer support.
- Self-driving cars navigating traffic.
- AI tools automating business workflows or IT tasks.
Why Are AI Agents a Big Deal?
They free up your time by handling repetitive or complex tasks, work 24/7, adapt to your needs, and can even collaborate with other agents to tackle bigger challenges.
In short: AI Agents are your digital teammates—always learning, always working, and always aiming to make your life easier! 😎
React ♥️ for more
❤9
Forwarded from Python Projects & Resources
𝟱 𝗠𝘂𝘀𝘁-𝗙𝗼𝗹𝗹𝗼𝘄 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝗳𝗼𝗿 𝗔𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Want to Become a Data Scientist in 2025? Start Here!🎯
If you’re serious about becoming a Data Scientist in 2025, the learning doesn’t have to be expensive — or boring!🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4kfBR5q
Perfect for beginners and aspiring pros✅️
Want to Become a Data Scientist in 2025? Start Here!🎯
If you’re serious about becoming a Data Scientist in 2025, the learning doesn’t have to be expensive — or boring!🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4kfBR5q
Perfect for beginners and aspiring pros✅️
❤1
Here are 7 ChatGPT Prompts to Elevate Your Skills to Superhuman Levels (PART 2):
1. Goal-Setting for Multiple Interests:
I have diverse interests in [insert multiple fields or hobbies]. Can you help me create a goal-setting strategy that allows me to pursue all of them effectively without feeling overwhelmed?
2. Rewrite in a Shakespearean Voice:
Transform this modern text [insert text] into something that could have been written by Shakespeare. Include rich metaphors, dramatic flair, and Elizabethan English to reflect his distinctive style.
3. Pomodoro Multitasking for Multiple Projects:
I have several overlapping projects in [insert field]. Can you help me create a Pomodoro Technique schedule that allows me to divide my time between each task without losing focus or momentum?
4. Curiosity-Driven Growth:
Design a mindset shift plan that encourages me to approach problems in [insert context] with curiosity instead of frustration. Include exercises that challenge my assumptions and foster a growth-oriented perspective.
5. Lead Magnet Launcher:
Assume the role of a digital marketing strategist. Suggest high-converting lead magnets that can be created in Canva for [insert business type], addressing specific audience pain points such as [insert common challenges].
6. Resume Transformation Expert:
Assume the role of a resume transformation expert. I’m updating my resume for a career change to [insert new field]. Can you help me restructure my resume to highlight my transferable skills, key accomplishments, and relevant experience that align with my new career goals?
7. Confidence-Building Specialist:
Assume the role of a confidence-building specialist. I often struggle with self-confidence in [insert context]. Can you design a 30-day confidence-boosting plan that includes positive affirmations, goal-setting, and small daily actions to build my confidence gradually?
1. Goal-Setting for Multiple Interests:
I have diverse interests in [insert multiple fields or hobbies]. Can you help me create a goal-setting strategy that allows me to pursue all of them effectively without feeling overwhelmed?
2. Rewrite in a Shakespearean Voice:
Transform this modern text [insert text] into something that could have been written by Shakespeare. Include rich metaphors, dramatic flair, and Elizabethan English to reflect his distinctive style.
3. Pomodoro Multitasking for Multiple Projects:
I have several overlapping projects in [insert field]. Can you help me create a Pomodoro Technique schedule that allows me to divide my time between each task without losing focus or momentum?
4. Curiosity-Driven Growth:
Design a mindset shift plan that encourages me to approach problems in [insert context] with curiosity instead of frustration. Include exercises that challenge my assumptions and foster a growth-oriented perspective.
5. Lead Magnet Launcher:
Assume the role of a digital marketing strategist. Suggest high-converting lead magnets that can be created in Canva for [insert business type], addressing specific audience pain points such as [insert common challenges].
6. Resume Transformation Expert:
Assume the role of a resume transformation expert. I’m updating my resume for a career change to [insert new field]. Can you help me restructure my resume to highlight my transferable skills, key accomplishments, and relevant experience that align with my new career goals?
7. Confidence-Building Specialist:
Assume the role of a confidence-building specialist. I often struggle with self-confidence in [insert context]. Can you design a 30-day confidence-boosting plan that includes positive affirmations, goal-setting, and small daily actions to build my confidence gradually?
❤5
Forwarded from Artificial Intelligence
🎓 𝗟𝗲𝗮𝗿𝗻 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗳𝗿𝗼𝗺 𝗛𝗮𝗿𝘃𝗮𝗿𝗱, 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱, 𝗠𝗜𝗧 & 𝗚𝗼𝗼𝗴𝗹𝗲😍
Why pay thousands when you can access world-class Computer Science courses for free? 🌐
Top institutions like Harvard, Stanford, MIT, and Google offer high-quality learning resources to help you master in-demand tech skills👨🎓📌
𝐋𝐢𝐧𝐤👇:-
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Perfect for students, self-learners, and career switchers✅️
Why pay thousands when you can access world-class Computer Science courses for free? 🌐
Top institutions like Harvard, Stanford, MIT, and Google offer high-quality learning resources to help you master in-demand tech skills👨🎓📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3ZyQpFd
Perfect for students, self-learners, and career switchers✅️
❤1
Common Machine Learning Algorithms!
1️⃣ Linear Regression
->Used for predicting continuous values.
->Models the relationship between dependent and independent variables by fitting a linear equation.
2️⃣ Logistic Regression
->Ideal for binary classification problems.
->Estimates the probability that an instance belongs to a particular class.
3️⃣ Decision Trees
->Splits data into subsets based on the value of input features.
->Easy to visualize and interpret but can be prone to overfitting.
4️⃣ Random Forest
->An ensemble method using multiple decision trees.
->Reduces overfitting and improves accuracy by averaging multiple trees.
5️⃣ Support Vector Machines (SVM)
->Finds the hyperplane that best separates different classes.
->Effective in high-dimensional spaces and for classification tasks.
6️⃣ k-Nearest Neighbors (k-NN)
->Classifies data based on the majority class among the k-nearest neighbors.
->Simple and intuitive but can be computationally intensive.
7️⃣ K-Means Clustering
->Partitions data into k clusters based on feature similarity.
->Useful for market segmentation, image compression, and more.
8️⃣ Naive Bayes
->Based on Bayes' theorem with an assumption of independence among predictors.
->Particularly useful for text classification and spam filtering.
9️⃣ Neural Networks
->Mimic the human brain to identify patterns in data.
->Power deep learning applications, from image recognition to natural language processing.
🔟 Gradient Boosting Machines (GBM)
->Combines weak learners to create a strong predictive model.
->Used in various applications like ranking, classification, and regression.
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
ENJOY LEARNING 👍👍
1️⃣ Linear Regression
->Used for predicting continuous values.
->Models the relationship between dependent and independent variables by fitting a linear equation.
2️⃣ Logistic Regression
->Ideal for binary classification problems.
->Estimates the probability that an instance belongs to a particular class.
3️⃣ Decision Trees
->Splits data into subsets based on the value of input features.
->Easy to visualize and interpret but can be prone to overfitting.
4️⃣ Random Forest
->An ensemble method using multiple decision trees.
->Reduces overfitting and improves accuracy by averaging multiple trees.
5️⃣ Support Vector Machines (SVM)
->Finds the hyperplane that best separates different classes.
->Effective in high-dimensional spaces and for classification tasks.
6️⃣ k-Nearest Neighbors (k-NN)
->Classifies data based on the majority class among the k-nearest neighbors.
->Simple and intuitive but can be computationally intensive.
7️⃣ K-Means Clustering
->Partitions data into k clusters based on feature similarity.
->Useful for market segmentation, image compression, and more.
8️⃣ Naive Bayes
->Based on Bayes' theorem with an assumption of independence among predictors.
->Particularly useful for text classification and spam filtering.
9️⃣ Neural Networks
->Mimic the human brain to identify patterns in data.
->Power deep learning applications, from image recognition to natural language processing.
🔟 Gradient Boosting Machines (GBM)
->Combines weak learners to create a strong predictive model.
->Used in various applications like ranking, classification, and regression.
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
ENJOY LEARNING 👍👍
❤2
Roadmap to Building AI Agents
1. Master Python Programming – Build a solid foundation in Python, the primary language for AI development.
2. Understand RESTful APIs – Learn how to send and receive data via APIs, a crucial part of building interactive agents.
3. Dive into Large Language Models (LLMs) – Get a grip on how LLMs work and how they power intelligent behavior.
4. Get Hands-On with the OpenAI API – Familiarize yourself with GPT models and tools like function calling and assistants.
5. Explore Vector Databases – Understand how to store and search high-dimensional data efficiently.
6. Work with Embeddings – Learn how to generate and query embeddings for context-aware responses.
7. Implement Caching and Persistent Memory – Use databases to maintain memory across interactions.
8. Build APIs with Flask or FastAPI – Serve your agents as web services using these Python frameworks.
9. Learn Prompt Engineering – Master techniques to guide and control LLM responses.
10. Study Retrieval-Augmented Generation (RAG) – Learn how to combine external knowledge with LLMs.
11. Explore Agentic Frameworks – Use tools like LangChain and LangGraph to structure your agents.
12. Integrate External Tools – Learn to connect agents to real-world tools and APIs (like using MCP).
13. Deploy with Docker – Containerize your agents for consistent and scalable deployment.
14. Control Agent Behavior – Learn how to set limits and boundaries to ensure reliable outputs.
15. Implement Safety and Guardrails – Build in mechanisms to ensure ethical and safe agent behavior.
React ❤️ for more
1. Master Python Programming – Build a solid foundation in Python, the primary language for AI development.
2. Understand RESTful APIs – Learn how to send and receive data via APIs, a crucial part of building interactive agents.
3. Dive into Large Language Models (LLMs) – Get a grip on how LLMs work and how they power intelligent behavior.
4. Get Hands-On with the OpenAI API – Familiarize yourself with GPT models and tools like function calling and assistants.
5. Explore Vector Databases – Understand how to store and search high-dimensional data efficiently.
6. Work with Embeddings – Learn how to generate and query embeddings for context-aware responses.
7. Implement Caching and Persistent Memory – Use databases to maintain memory across interactions.
8. Build APIs with Flask or FastAPI – Serve your agents as web services using these Python frameworks.
9. Learn Prompt Engineering – Master techniques to guide and control LLM responses.
10. Study Retrieval-Augmented Generation (RAG) – Learn how to combine external knowledge with LLMs.
11. Explore Agentic Frameworks – Use tools like LangChain and LangGraph to structure your agents.
12. Integrate External Tools – Learn to connect agents to real-world tools and APIs (like using MCP).
13. Deploy with Docker – Containerize your agents for consistent and scalable deployment.
14. Control Agent Behavior – Learn how to set limits and boundaries to ensure reliable outputs.
15. Implement Safety and Guardrails – Build in mechanisms to ensure ethical and safe agent behavior.
React ❤️ for more
❤7
𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗼𝗻 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 – 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗣𝗹𝗮𝘆𝗹𝗶𝘀𝘁 𝗚𝘂𝗶𝗱𝗲😍
🎥 YouTube is the ultimate free classroom—and this is your Data Analytics syllabus in one post!👨💻
From Python and SQL to Power BI, Machine Learning, and Data Science, these carefully curated playlists will take you from complete beginner to job-ready✨️📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jzVggc
Enjoy Learning ✅️
🎥 YouTube is the ultimate free classroom—and this is your Data Analytics syllabus in one post!👨💻
From Python and SQL to Power BI, Machine Learning, and Data Science, these carefully curated playlists will take you from complete beginner to job-ready✨️📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jzVggc
Enjoy Learning ✅️
❤2
LLM Cheatsheet
Introduction to LLMs
- LLMs (Large Language Models) are AI systems that generate text by predicting the next word.
- Prompts are the instructions or text you give to an LLM.
- Personas allow LLMs to take on specific roles or tones.
- Learning types:
- Zero-shot (no examples given)
- One-shot (one example)
- Few-shot (a few examples)
Transformers
- The core architecture behind LLMs, using self-attention to process input sequences.
- Encoder: Understands input.
- Decoder: Generates output.
- Embeddings: Converts words into vectors.
Types of LLMs
- Encoder-only: Great for understanding (like BERT).
- Decoder-only: Best for generating text (like GPT).
- Encoder-decoder: Useful for tasks like translation and summarization (like T5).
Configuration Settings
- Decoding strategies:
- Greedy: Always picks the most likely next word.
- Beam search: Considers multiple possible sequences.
- Random sampling: Adds creativity by picking among top choices.
- Temperature: Controls randomness (higher value = more creative output).
- Top-k and Top-p: Restrict choices to the most likely words.
LLM Instruction Fine-Tuning & Evaluation
- Instruction fine-tuning: Trains LLMs to follow specific instructions.
- Task-specific fine-tuning: Focuses on a single task.
- Multi-task fine-tuning: Trains on multiple tasks for broader skills.
Model Evaluation
- Evaluating LLMs is hard-metrics like BLEU and ROUGE are common, but human judgment is often needed.
Join our WhatsApp Channel: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
Introduction to LLMs
- LLMs (Large Language Models) are AI systems that generate text by predicting the next word.
- Prompts are the instructions or text you give to an LLM.
- Personas allow LLMs to take on specific roles or tones.
- Learning types:
- Zero-shot (no examples given)
- One-shot (one example)
- Few-shot (a few examples)
Transformers
- The core architecture behind LLMs, using self-attention to process input sequences.
- Encoder: Understands input.
- Decoder: Generates output.
- Embeddings: Converts words into vectors.
Types of LLMs
- Encoder-only: Great for understanding (like BERT).
- Decoder-only: Best for generating text (like GPT).
- Encoder-decoder: Useful for tasks like translation and summarization (like T5).
Configuration Settings
- Decoding strategies:
- Greedy: Always picks the most likely next word.
- Beam search: Considers multiple possible sequences.
- Random sampling: Adds creativity by picking among top choices.
- Temperature: Controls randomness (higher value = more creative output).
- Top-k and Top-p: Restrict choices to the most likely words.
LLM Instruction Fine-Tuning & Evaluation
- Instruction fine-tuning: Trains LLMs to follow specific instructions.
- Task-specific fine-tuning: Focuses on a single task.
- Multi-task fine-tuning: Trains on multiple tasks for broader skills.
Model Evaluation
- Evaluating LLMs is hard-metrics like BLEU and ROUGE are common, but human judgment is often needed.
Join our WhatsApp Channel: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
❤2
𝗦𝗤𝗟 𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
Looking to master SQL for Data Analytics or prep for your dream tech job? 💼
These 3 Free SQL resources will help you go from beginner to job-ready—without spending a single rupee! 📊✨
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3TcvfsA
💥 Start learning today and build the skills top companies want!✅️
Looking to master SQL for Data Analytics or prep for your dream tech job? 💼
These 3 Free SQL resources will help you go from beginner to job-ready—without spending a single rupee! 📊✨
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3TcvfsA
💥 Start learning today and build the skills top companies want!✅️
Python Detailed Roadmap 🚀
📌 1. Basics
◼ Data Types & Variables
◼ Operators & Expressions
◼ Control Flow (if, loops)
📌 2. Functions & Modules
◼ Defining Functions
◼ Lambda Functions
◼ Importing & Creating Modules
📌 3. File Handling
◼ Reading & Writing Files
◼ Working with CSV & JSON
📌 4. Object-Oriented Programming (OOP)
◼ Classes & Objects
◼ Inheritance & Polymorphism
◼ Encapsulation
📌 5. Exception Handling
◼ Try-Except Blocks
◼ Custom Exceptions
📌 6. Advanced Python Concepts
◼ List & Dictionary Comprehensions
◼ Generators & Iterators
◼ Decorators
📌 7. Essential Libraries
◼ NumPy (Arrays & Computations)
◼ Pandas (Data Analysis)
◼ Matplotlib & Seaborn (Visualization)
📌 8. Web Development & APIs
◼ Web Scraping (BeautifulSoup, Scrapy)
◼ API Integration (Requests)
◼ Flask & Django (Backend Development)
📌 9. Automation & Scripting
◼ Automating Tasks with Python
◼ Working with Selenium & PyAutoGUI
📌 10. Data Science & Machine Learning
◼ Data Cleaning & Preprocessing
◼ Scikit-Learn (ML Algorithms)
◼ TensorFlow & PyTorch (Deep Learning)
📌 11. Projects
◼ Build Real-World Applications
◼ Showcase on GitHub
📌 12. ✅ Apply for Jobs
◼ Strengthen Resume & Portfolio
◼ Prepare for Technical Interviews
Like for more ❤️💪
📌 1. Basics
◼ Data Types & Variables
◼ Operators & Expressions
◼ Control Flow (if, loops)
📌 2. Functions & Modules
◼ Defining Functions
◼ Lambda Functions
◼ Importing & Creating Modules
📌 3. File Handling
◼ Reading & Writing Files
◼ Working with CSV & JSON
📌 4. Object-Oriented Programming (OOP)
◼ Classes & Objects
◼ Inheritance & Polymorphism
◼ Encapsulation
📌 5. Exception Handling
◼ Try-Except Blocks
◼ Custom Exceptions
📌 6. Advanced Python Concepts
◼ List & Dictionary Comprehensions
◼ Generators & Iterators
◼ Decorators
📌 7. Essential Libraries
◼ NumPy (Arrays & Computations)
◼ Pandas (Data Analysis)
◼ Matplotlib & Seaborn (Visualization)
📌 8. Web Development & APIs
◼ Web Scraping (BeautifulSoup, Scrapy)
◼ API Integration (Requests)
◼ Flask & Django (Backend Development)
📌 9. Automation & Scripting
◼ Automating Tasks with Python
◼ Working with Selenium & PyAutoGUI
📌 10. Data Science & Machine Learning
◼ Data Cleaning & Preprocessing
◼ Scikit-Learn (ML Algorithms)
◼ TensorFlow & PyTorch (Deep Learning)
📌 11. Projects
◼ Build Real-World Applications
◼ Showcase on GitHub
📌 12. ✅ Apply for Jobs
◼ Strengthen Resume & Portfolio
◼ Prepare for Technical Interviews
Like for more ❤️💪
❤2
The Singularity is near—our world will soon change forever! Are you ready? Read the Manifesto now and secure your place in the future: https://aism.faith Subscribe to the channel: https://news.1rj.ru/str/aism
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Forwarded from Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
𝗦𝗤𝗟:- https://pdlink.in/3TcvfsA
𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:- https://pdlink.in/3Hfpwjc
𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:- https://pdlink.in/3ZyQpFd
𝗣𝘆𝘁𝗵𝗼𝗻 :- https://pdlink.in/3Hnx3wh
𝗗𝗲𝘃𝗢𝗽𝘀 :- https://pdlink.in/4jyxBwS
𝗪𝗲𝗯 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 :- https://pdlink.in/4jCAtJ5
Enroll for FREE & Get Certified 🎓
𝗦𝗤𝗟:- https://pdlink.in/3TcvfsA
𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:- https://pdlink.in/3Hfpwjc
𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗦𝗰𝗶𝗲𝗻𝗰𝗲:- https://pdlink.in/3ZyQpFd
𝗣𝘆𝘁𝗵𝗼𝗻 :- https://pdlink.in/3Hnx3wh
𝗗𝗲𝘃𝗢𝗽𝘀 :- https://pdlink.in/4jyxBwS
𝗪𝗲𝗯 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 :- https://pdlink.in/4jCAtJ5
Enroll for FREE & Get Certified 🎓
Want to become an Agent AI Expert in 2025?
🤩AI isn’t just evolving—it’s transforming industries. And agentic AI is leading the charge!
Here’s your 6-step guide to mastering it:
1️⃣ Master AI Fundamentals – Python, TensorFlow & PyTorch 📊
2️⃣ Understand Agentic Systems – Learn reinforcement learning 🧠
3️⃣ Get Hands-On with Projects – OpenAI Gym & Rasa 🔍
4️⃣ Learn Prompt Engineering – Tools like ChatGPT & LangChain ⚙️
5️⃣ Stay Updated – Follow Arxiv, GitHub & AI newsletters 📰
6️⃣ Join AI Communities – Engage in forums like Reddit & Discord 🌐
🤩AI isn’t just evolving—it’s transforming industries. And agentic AI is leading the charge!
Here’s your 6-step guide to mastering it:
1️⃣ Master AI Fundamentals – Python, TensorFlow & PyTorch 📊
2️⃣ Understand Agentic Systems – Learn reinforcement learning 🧠
3️⃣ Get Hands-On with Projects – OpenAI Gym & Rasa 🔍
4️⃣ Learn Prompt Engineering – Tools like ChatGPT & LangChain ⚙️
5️⃣ Stay Updated – Follow Arxiv, GitHub & AI newsletters 📰
6️⃣ Join AI Communities – Engage in forums like Reddit & Discord 🌐
🎯 AI Agent is all about creating intelligent systems that can make decisions autonomously—perfect for businesses aiming to scale with minimal human intervention.
❤4
Forwarded from Artificial Intelligence
𝟱 𝗙𝗿𝗲𝗲 𝗠𝗜𝗧 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗧𝗮𝗸𝗲 𝗢𝗻𝗹𝗶𝗻𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍
🎓No MIT Admission? No Problem — Learn from MIT for Free!🔥
MIT is known for world-class education—but you don’t need to walk its halls to access its knowledge📚📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jBNtP2
These courses offer industry-relevant skills & completion certificates at no cost✅️
🎓No MIT Admission? No Problem — Learn from MIT for Free!🔥
MIT is known for world-class education—but you don’t need to walk its halls to access its knowledge📚📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jBNtP2
These courses offer industry-relevant skills & completion certificates at no cost✅️
❤1
For those who feel like they're not learning much and feeling demotivated. You should definitely read these lines from one of the book by Andrew Ng 👇
No one can cram everything they need to know over a weekend or even a month. Everyone I
know who’s great at machine learning is a lifelong learner. Given how quickly our field is changing,
there’s little choice but to keep learning if you want to keep up.
How can you maintain a steady pace of learning for years? If you can cultivate the habit of
learning a little bit every week, you can make significant progress with what feels like less effort.
Everyday it gets easier but you need to do it everyday ❤️
No one can cram everything they need to know over a weekend or even a month. Everyone I
know who’s great at machine learning is a lifelong learner. Given how quickly our field is changing,
there’s little choice but to keep learning if you want to keep up.
How can you maintain a steady pace of learning for years? If you can cultivate the habit of
learning a little bit every week, you can make significant progress with what feels like less effort.
Everyday it gets easier but you need to do it everyday ❤️
❤4
𝟱 𝗣𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝗶𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲😍
Looking to Master Python for Free?✨️
These 5 GitHub repositories are all you need to level up — from beginner to advanced! 💻
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3FG7DcW
📌 Save this post & share it with a Python learner!
Looking to Master Python for Free?✨️
These 5 GitHub repositories are all you need to level up — from beginner to advanced! 💻
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3FG7DcW
📌 Save this post & share it with a Python learner!
❤1
𝟲 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗧𝗼 𝗖𝗵𝗮𝗻𝗴𝗲 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗜𝗻 𝟮𝟬𝟮𝟱 😍
🎯 Want to switch careers or upgrade your skills — without spending a single rupee?
Check out 6 handpicked, beginner-friendly courses in high-demand fields like Data Science, Web Development, Digital Marketing, Project Management, and more. 🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4e1I17a
💥 Start learning today and build the skills top companies want!✅️
🎯 Want to switch careers or upgrade your skills — without spending a single rupee?
Check out 6 handpicked, beginner-friendly courses in high-demand fields like Data Science, Web Development, Digital Marketing, Project Management, and more. 🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4e1I17a
💥 Start learning today and build the skills top companies want!✅️
The Rise of Generative AI in Data Analytics
Today, let’s talk about how Generative AI is reshaping the field of Data Analytics and what this means for YOU as a data professional!
What is Generative AI in Data Analytics Context?
Generative AI refers to AI models that can generate text, code, images, and even data insights based on patterns.
Tools like ChatGPT, Bard, Copilot, and Claude are now being used to:
✅ Automate data cleaning & transformation
✅ Generate SQL & Python noscripts for complex queries
✅ Build interactive dashboards with natural language commands
✅ Provide explainable insights without deep statistical knowledge
How Businesses Are Using AI-Powered Analytics
📊 Retail & E-commerce – AI predicts sales trends and personalizes recommendations.
🏦 Finance & Banking – Fraud detection using AI-powered anomaly detection.
🩺 Healthcare – AI analyzes patient data for early disease detection.
📈 Marketing & Advertising – AI automates customer segmentation and sentiment analysis.
Should Data Analysts Be Worried?
NO! Instead of replacing data analysts, AI enhances their work by:
🚀 Speeding up data preparation
🔍 Enhancing insights generation
🤖 Reducing manual repetitive tasks
How You Can Adapt & Stay Ahead
🔹 Learn AI-powered tools like Power BI’s Copilot, ChatGPT for SQL, and AutoML.
🔹 Improve prompt engineering to interact effectively with AI.
🔹 Focus on critical thinking & domain knowledge—AI can’t replace human intuition!
Generative AI is a game-changer, but the human touch in analytics will always be needed! Instead of fearing AI, use it as your assistant. The future belongs to those who learn, adapt, and innovate.
Here are some telegram channels related to artificial Intelligence and generative AI which will help you with free resources:
https://news.1rj.ru/str/generativeai_gpt
https://news.1rj.ru/str/machinelearning_deeplearning
https://news.1rj.ru/str/AI_Best_Tools
https://news.1rj.ru/str/aichads
https://news.1rj.ru/str/aiindi
Last one is my favourite ❤️
React with ❤️ if you want me to continue posting on such interesting & useful topics
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Today, let’s talk about how Generative AI is reshaping the field of Data Analytics and what this means for YOU as a data professional!
What is Generative AI in Data Analytics Context?
Generative AI refers to AI models that can generate text, code, images, and even data insights based on patterns.
Tools like ChatGPT, Bard, Copilot, and Claude are now being used to:
✅ Automate data cleaning & transformation
✅ Generate SQL & Python noscripts for complex queries
✅ Build interactive dashboards with natural language commands
✅ Provide explainable insights without deep statistical knowledge
How Businesses Are Using AI-Powered Analytics
📊 Retail & E-commerce – AI predicts sales trends and personalizes recommendations.
🏦 Finance & Banking – Fraud detection using AI-powered anomaly detection.
🩺 Healthcare – AI analyzes patient data for early disease detection.
📈 Marketing & Advertising – AI automates customer segmentation and sentiment analysis.
Should Data Analysts Be Worried?
NO! Instead of replacing data analysts, AI enhances their work by:
🚀 Speeding up data preparation
🔍 Enhancing insights generation
🤖 Reducing manual repetitive tasks
How You Can Adapt & Stay Ahead
🔹 Learn AI-powered tools like Power BI’s Copilot, ChatGPT for SQL, and AutoML.
🔹 Improve prompt engineering to interact effectively with AI.
🔹 Focus on critical thinking & domain knowledge—AI can’t replace human intuition!
Generative AI is a game-changer, but the human touch in analytics will always be needed! Instead of fearing AI, use it as your assistant. The future belongs to those who learn, adapt, and innovate.
Here are some telegram channels related to artificial Intelligence and generative AI which will help you with free resources:
https://news.1rj.ru/str/generativeai_gpt
https://news.1rj.ru/str/machinelearning_deeplearning
https://news.1rj.ru/str/AI_Best_Tools
https://news.1rj.ru/str/aichads
https://news.1rj.ru/str/aiindi
Last one is my favourite ❤️
React with ❤️ if you want me to continue posting on such interesting & useful topics
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤1
𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 𝘄𝗶𝘁𝗵 𝗛𝗮𝗿𝘃𝗮𝗿𝗱 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆😍
🎯 Want to break into Data Science without spending a single rupee?💰
Harvard University is offering a goldmine of free courses that make top-tier education accessible to anyone, anywhere👨💻✨️
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3HxOgTW
These courses are designed by Ivy League experts and are trusted by thousands globally✅️
🎯 Want to break into Data Science without spending a single rupee?💰
Harvard University is offering a goldmine of free courses that make top-tier education accessible to anyone, anywhere👨💻✨️
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3HxOgTW
These courses are designed by Ivy League experts and are trusted by thousands globally✅️