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

Double Tap ❤️ for more

✅ Roadmap To Learn Gen AI: Step-by-Step Guide

1• Grasp the Basics of AI
◦ Understand AI, ML, DL differences
◦ Learn types of AI: narrow, general, super
◦ Explore real-world AI applications

2• Learn Python for AI
◦ Master Python fundamentals
◦ Use libraries: NumPy, Pandas, Matplotlib
◦ Learn basic data preprocessing

3• Master Machine Learning Concepts
◦ Supervised vs. Unsupervised learning
◦ Regression, classification, clustering
◦ Overfitting, underfitting, bias-variance tradeoff

4• Dive Into Deep Learning
◦ Neural networks: forward & backpropagation
◦ Activation functions, loss functions
◦ Use TensorFlow or PyTorch

5• Understand Transformers
◦ Learn about self-attention mechanisms
◦ Understand encoder, decoder, positional encoding
◦ Study “Attention is All You Need” paper

6• Explore Language Modeling
◦ Learn tokenization & embeddings
◦ Understand masked vs. causal language models
◦ Study next-token prediction

7• Get Started with Models
◦ Learn how -2, -3, -4 work
◦ Explore OpenAI Playground
◦ Experiment with Chat

8• Learn About BERT and Encoder-Based Models
◦ Understand masked language modeling
◦ Use BERT for classification, QA tasks
◦ Explore Hugging Face Transformers

9• Dive Into Generative Models
◦ Study GANs, VAEs, Diffusion Models
◦ Understand use cases: image, audio, video

10• Practice Prompt Engineering
◦ Use zero-shot, few-shot, chain-of-thought prompting
◦ Learn how prompt structure affects output
◦ Experiment with different prompt styles

11• Build With OpenAI & Hugging Face
◦ Use OpenAI API (Chat, DALL·E, Whisper)
◦ Learn about Hugging Face Spaces & Models
◦ Deploy simple GenAI apps

12• Work With LangChain
◦ Build AI pipelines with LangChain
◦ Use agents, memory, tools
◦ Connect LLMs with external data sources

13• Create Real-World GenAI Projects
◦ Build AI content writers, chatbots
◦ Try text-to-image, text-to-code apps
◦ Use pre-built APIs to accelerate development

14• Learn RAG (Retrieval-Augmented Generation)
◦ Understand how LLMs retrieve/generate answers
◦ Use tools like LlamaIndex, Haystack
◦ Connect with vector databases (e.g., Pinecone)

15• Experiment with Fine-Tuning
◦ Learn difference between fine-tuning and prompt engineering
◦ Try LoRA, PEFT for efficient training
◦ Use domain-specific datasets

16• Explore Multi-Modal GenAI
◦ Work with tools like -4V, ChatGPT, LLaVA
◦ Learn image-to-text, text-to-image models
◦ Understand use cases in design, vision, more

17• Study Ethics & AI Safety
◦ Understand AI bias, explainability
◦ Explore safety practices & fairness
◦ Learn about responsible AI deployment

18• Build AI Agents & Workflows
◦ Use tools like Auto-, CrewAI, OpenAgents
◦ Create workflows for automation
◦ Deploy agents for real-world tasks

19• Join AI Communities
◦ Engage on Hugging Face, Discord, Reddit, Twitter
◦ Follow top AI researchers
◦ Contribute to open-source tools

20• Stay Updated & Keep Experimenting
◦ Read research papers, attend conferences
◦ Keep testing new APIs, models, frameworks
◦ Continuously build & share your work

👍 React ❤️ for more

🏆 – AI/ML Engineer

Stage 1 – Python Basics
Stage 2 – Statistics & Probability
Stage 3 – Linear Algebra & Calculus
Stage 4 – Data Preprocessing
Stage 5 – Exploratory Data Analysis (EDA)
Stage 6 – Supervised Learning
Stage 7 – Unsupervised Learning
Stage 8 – Feature Engineering
Stage 9 – Model Evaluation & Tuning
Stage 10 – Deep Learning Basics
Stage 11 – Neural Networks & CNNs
Stage 12 – RNNs & LSTMs
Stage 13 – NLP Fundamentals
Stage 14 – Deployment (Flask, Docker)
Stage 15 – Build projects

Master Artificial Intelligence in 10 days with free resources 😄👇

Day 1: Introduction to AI
- Start with an overview of what AI is and its various applications.
- Read articles or watch videos explaining the basics of AI.

Day 2-3: Machine Learning Fundamentals
- Learn the basics of machine learning, including supervised and unsupervised learning.
- Study concepts like data, features, labels, and algorithms.

Day 4-5: Deep Learning
- Dive into deep learning, understanding neural networks and their architecture.
- Learn about popular deep learning frameworks like TensorFlow or PyTorch.

Day 6: Natural Language Processing (NLP)
- Explore the basics of NLP, including tokenization, sentiment analysis, and named entity recognition.

Day 7: Computer Vision
- Study computer vision, including image recognition, object detection, and convolutional neural networks.

Day 8: AI Ethics and Bias
- Explore the ethical considerations in AI and the issue of bias in AI algorithms.

Day 9: AI Tools and Resources
- Familiarize yourself with AI development tools and platforms.
- Learn how to access and use AI datasets and APIs.

Day 10: AI Project
- Work on a small AI project. For example, build a basic chatbot, create an image classifier, or analyze a dataset using AI techniques.

Here are 5 amazing AI projects with free datasets: https://bit.ly/3ZVDjR1

Throughout the 10 days, it's important to practice what you learn through coding and practical exercises. Additionally, consider reading AI-related books and articles, watching online courses, and participating in AI communities and forums to enhance your learning experience.

Free Books and Courses to Learn Artificial Intelligence
👇👇

Introduction to AI Free Udacity Course

Introduction to Prolog programming for artificial intelligence Free Book

Introduction to AI for Business Free Course

Artificial Intelligence: Foundations of Computational Agents Free Book

Learn Basics about AI Free Udemy Course
(4.4 Star ratings out of 5)

Amazing AI Reverse Image Search
(4.7 Star ratings out of 5)

13 AI Tools to improve your productivity: https://t.iss.one/crackingthecodinginterview/619

4 AI Certifications for Developers: https://t.iss.one/datasciencefun/1375

Join @free4unow_backup for more free courses

ENJOY LEARNING👍👍

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 ❤️

OnSpace Mobile App builder: Idea → AppStore → Profit.
👉https://onspace.ai/?via=tg_ggpt
With OnSpace, you can turn your idea into a real iOS or Android app in AppStore/PlayStore.

What will you get:
- Create app by chatting with AI
- Real-time app demo.
- Add payments and monetize like in-app-purchase and Stripe.
- Functional login & signup.
- Database + dashboard in minutes.
- Preview, download, and publish to AppStore.
- Full tutorial on YouTube and within 1 day customer service

🫵It’s your shortcut from concept to cash flow.