AI Agent GuideBook 🚀

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🚀 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮𝗻 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 — 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺

Master the hottest skill in tech: building intelligent AI systems that think and act independently.
Join Ready Tensor’s free, hands-on program to create three portfolio-grade projects: RAG systems → Multi-agent workflows → Production deployment.

𝗘𝗮𝗿𝗻 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗰𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 and 𝗴𝗲𝘁 𝗻𝗼𝘁𝗶𝗰𝗲𝗱 𝗯𝘆 𝘁𝗼𝗽 𝗔𝗜 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗿𝘀.

𝗙𝗿𝗲𝗲. 𝗦𝗲𝗹𝗳-𝗽𝗮𝗰𝗲𝗱. 𝗖𝗮𝗿𝗲𝗲𝗿-𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴.

👉 Join today: https://go.readytensor.ai/cert-544-agentic-ai-certification

How to use AI For Job Search

Software Engineers vs AI Engineers: 👊

Software engineers are often shocked when they learn of AI engineers' salaries. There are two reasons for this surprise.

1. The total compensation for AI engineers is jaw-dropping. You can check it out at AIPaygrad.es, which has manually verified data for AI engineers. The median overall compensation for a “Novice” is $328,350/year.
2. AI engineers are no smarter than software engineers. You figure this out only after a friend or acquaintance upskills and finds a lucrative AI job.

The biggest difference between Software and AI engineers is the demand for such roles. One role is declining, and the other is reaching stratospheric heights.

Here is an example.

Just last week, we saw an implosion of OpenAI after Sam Altman was unceremoniously removed from his CEO position. About 95% of their AI Engineers threatened to quit in protest. Rumor had it that these 700 engineers had an open job offer from Microsoft. 🚀

Contrast this with the events a few months back. Microsoft laid off 10,000 Software Engineers while setting aside $10B to invest in OpenAI. They cut these jobs despite making stunning profits in 2023.

In conclusion, these events underline a significant shift in the tech industry. For software engineers, it's a call to adapt and possibly upskill in AI, while companies need to balance AI investments with nurturing their current talent. The future of tech hinges on flexibility and continuous learning for everyone involved."

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 scripts 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://t.iss.one/generativeai_gpt

https://t.iss.one/machinelearning_deeplearning

https://t.iss.one/AI_Best_Tools

https://t.iss.one/aichads

https://t.iss.one/aiindi

Last one is my favourite ❤️

React with ❤️ if you want me to continue posting on such interesting & useful topics

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)

⬇️ Pysentimiento: A Python toolkit for Sentiment Analysis and Social NLP tasks

Github: https://github.com/pysentimiento/pysentimiento

Paper: https://arxiv.org/abs/2106.09462

English model: https://huggingface.co/finiteautomata/bertweet-base-sentiment-analysis

Important Topics to become a data scientist
[Advanced Level]
👇👇

1. Mathematics

Linear Algebra
Analytic Geometry
Matrix
Vector Calculus
Optimization
Regression
Dimensionality Reduction
Density Estimation
Classification

2. Probability

Introduction to Probability
1D Random Variable
The function of One Random Variable
Joint Probability Distribution
Discrete Distribution
Normal Distribution

3. Statistics

Introduction to Statistics
Data Description
Random Samples
Sampling Distribution
Parameter Estimation
Hypotheses Testing
Regression

4. Programming

Python:

Python Basics
List
Set
Tuples
Dictionary
Function
NumPy
Pandas
Matplotlib/Seaborn

R Programming:

R Basics
Vector
List
Data Frame
Matrix
Array
Function
dplyr
ggplot2
Tidyr
Shiny

DataBase:
SQL
MongoDB

Data Structures

Web scraping

Linux

Git

5. Machine Learning

How Model Works
Basic Data Exploration
First ML Model
Model Validation
Underfitting & Overfitting
Random Forest
Handling Missing Values
Handling Categorical Variables
Pipelines
Cross-Validation(R)
XGBoost(Python|R)
Data Leakage

6. Deep Learning

Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
TensorFlow
Keras
PyTorch
A Single Neuron
Deep Neural Network
Stochastic Gradient Descent
Overfitting and Underfitting
Dropout Batch Normalization
Binary Classification

7. Feature Engineering

Baseline Model
Categorical Encodings
Feature Generation
Feature Selection

8. Natural Language Processing

Text Classification
Word Vectors

9. Data Visualization Tools

BI (Business Intelligence):
Tableau
Power BI
Qlik View
Qlik Sense

10. Deployment

Microsoft Azure
Heroku
Google Cloud Platform
Flask
Django

Machine learning is a subset of artificial intelligence that involves developing algorithms and models that enable computers to learn from and make predictions or decisions based on data. In machine learning, computers are trained on large datasets to identify patterns, relationships, and trends without being explicitly programmed to do so.

There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm is trained on labeled data, where the correct output is provided along with the input data. Unsupervised learning involves training the algorithm on unlabeled data, allowing it to identify patterns and relationships on its own. Reinforcement learning involves training an algorithm to make decisions by rewarding or punishing it based on its actions.

Machine learning algorithms can be used for a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, predictive analytics, and more. These algorithms can be trained using various techniques such as neural networks, decision trees, support vector machines, and clustering algorithms.

Join for more: t.iss.one/datasciencefun

🔰 How to become a data scientist in 2025?

👨🏻‍💻 If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.


🔢 Step 1: Strengthen your math and statistics!

✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:

✅ Linear algebra: matrices, vectors, eigenvalues.

🔗 Course: MIT 18.06 Linear Algebra


✅ Calculus: derivative, integral, optimization.

🔗 Course: MIT Single Variable Calculus


✅ Statistics and probability: Bayes' theorem, hypothesis testing.

🔗 Course: Statistics 110

➖➖➖➖➖

🔢 Step 2: Learn to code.

✏️ Learn Python and become proficient in coding. The most important topics you need to master are:

✅ Python: Pandas, NumPy, Matplotlib libraries

🔗 Course: FreeCodeCamp Python Course

✅ SQL language: Join commands, Window functions, query optimization.

🔗 Course: Stanford SQL Course

✅ Data structures and algorithms: arrays, linked lists, trees.

🔗 Course: MIT Introduction to Algorithms

➖➖➖➖➖

🔢 Step 3: Clean and visualize data

✏️ Learn how to process and clean data and then create an engaging story from it!

✅ Data cleaning: Working with missing values ​​and detecting outliers.

🔗 Course: Data Cleaning

✅ Data visualization: Matplotlib, Seaborn, Tableau

🔗 Course: Data Visualization Tutorial

➖➖➖➖➖

🔢 Step 4: Learn Machine Learning

✏️ It's time to enter the exciting world of machine learning! You should know these topics:

✅ Supervised learning: regression, classification.

✅ Unsupervised learning: clustering, PCA, anomaly detection.

✅ Deep learning: neural networks, CNN, RNN


🔗 Course: CS229: Machine Learning

➖➖➖➖➖

🔢 Step 5: Working with Big Data and Cloud Technologies

✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.

✅ Big Data Tools: Hadoop, Spark, Dask

✅ Cloud platforms: AWS, GCP, Azure

🔗 Course: Data Engineering

➖➖➖➖➖

🔢 Step 6: Do real projects!

✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.

✅ Kaggle competitions: solving real-world challenges.

✅ End-to-End projects: data collection, modeling, implementation.

✅ GitHub: Publish your projects on GitHub.

🔗 Platform: Kaggle🔗 Platform: ods.ai

➖➖➖➖➖

🔢 Step 7: Learn MLOps and deploy models

✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.

✅ MLOps training: model versioning, monitoring, model retraining.

✅ Deployment models: Flask, FastAPI, Docker

🔗 Course: Stanford MLOps Course

➖➖➖➖➖

🔢 Step 8: Stay up to date and network

✏️ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field.

✅ Read scientific articles: arXiv, Google Scholar

✅ Connect with the data community:

🔗 Site: Papers with code
🔗 Site: AI Research at Google

#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #data

✅ 5 Powerful Ways to Use Agentic AI 🤖

1️⃣ Prompt Routing 
▪️ Agent decides how to handle your request:
⦁ Respond directly
⦁ Search internet/APIs
⦁ Check internal docs
⦁ Combine all strategies

2️⃣ Query Writing 
▪️ Turns vague prompts into precise queries:
⦁ Build exact database/vector queries
⦁ Expand keywords
⦁ Convert to SQL/API calls
⦁ Optimize for relevance

3️⃣ Data Processing 
▪️ Cleans & preps your data:
⦁ Remove inconsistencies
⦁ Reformat for clarity
⦁ Add context & metadata
⦁ Summarize large datasets

4️⃣ Tool Orchestration 
▪️ Picks & connects tools smartly:
⦁ Choose best tool per task
⦁ Chain multiple tools together
⦁ Handle failures & adapt dynamically

5️⃣ Decision Support & Planning 
▪️ Breaks complex goals into steps:
⦁ Smaller, doable actions
⦁ Simulate options
⦁ Recommend logical next moves

✨ Agentic AI = Smarter, Faster, Autonomous Systems

💬 Like ❤️ & Share if this helped!

Here are the top 5 machine learning projects that are suitable for freshers to work on:

1. Predicting House Prices: Build a machine learning model that predicts house prices based on features such as location, size, number of bedrooms, etc. This project will help you understand regression techniques and feature engineering.

2. Image Classification: Create a model that can classify images into different categories such as cats vs. dogs, fruits, or handwritten digits. This project will introduce you to convolutional neural networks (CNNs) and image processing.

3. Sentiment Analysis: Develop a sentiment analysis model that can classify text data as positive, negative, or neutral. This project will help you learn natural language processing techniques and text classification algorithms.

4. Credit Card Fraud Detection: Build a model that can detect fraudulent credit card transactions based on transaction data. This project will help you understand anomaly detection techniques and imbalanced classification problems.

5. Recommendation System: Create a recommendation system that suggests products or movies to users based on their preferences and behavior. This project will introduce you to collaborative filtering and recommendation algorithms.

Credits: https://t.iss.one/free4unow_backup

All the best 👍👍

📌 5 AI Agent Projects to Try This Weekend

🔹 1. Image Collage Generator with ChatGPT Agents

👉 Try it: Ask ChatGPT to collect benchmark images from this page
, arrange them into a 16:9 collage, and outline agent results in red.
📖 Guide: ChatGPT Agent

🔹 2. Language Tutor with Langflow
👉 Drag & drop flows in Langflow to generate texts, add words, and keep practice interactive.
📖 Guide: Langflow

🔹 3. Data Analyst with Flowise
👉 Use Flowise nodes to connect MySQL → SQL prompt → LLM → results.
📖 Guide: Flowise

🔹 4. Medical Prescription Analyzer with Grok 4
👉 Powered by Grok 4 + Firecrawl + Gradio UI.
📖 Guide: Grok 4

🔹 5. Custom AI Agent with LangGraph + llama.cpp
👉 Use llama.cpp with LangGraph’s ReAct agent + Tavily search + Python REPL.
📖 Guide: llama.cpp

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