𝟰 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝘂𝗹𝗲𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀😍

Generative AI is no longer just a buzzword—it’s a career-maker🧑‍💻📌

Recruiters are actively looking for candidates with prompt engineering skills, hands-on AI experience, and the ability to use tools like GitHub Copilot and Azure OpenAI effectively.🖥

𝐋𝐢𝐧𝐤👇:-

http://pdlink.in/4fKT5pL

If you’re looking to stand out in interviews, land AI-powered roles, or future-proof your career, this is your chance

Essential Tools, Libraries, and Frameworks to learn Artificial Intelligence

1. Programming Languages:

Python

R

Java

Julia


2. AI Frameworks:

TensorFlow

PyTorch

Keras

MXNet

Caffe


3. Machine Learning Libraries:

Scikit-learn: For classical machine learning models.

XGBoost: For boosting algorithms.

LightGBM: For gradient boosting models.


4. Deep Learning Tools:

TensorFlow

PyTorch

Keras

Theano


5. Natural Language Processing (NLP) Tools:

NLTK (Natural Language Toolkit)

SpaCy

Hugging Face Transformers

Gensim


6. Computer Vision Libraries:

OpenCV

DLIB

Detectron2


7. Reinforcement Learning Frameworks:

Stable-Baselines3

RLlib

OpenAI Gym


8. AI Development Platforms:

IBM Watson

Google AI Platform

Microsoft AI


9. Data Visualization Tools:

Matplotlib

Seaborn

Plotly

Tableau


10. Robotics Frameworks:

ROS (Robot Operating System)

MoveIt!


11. Big Data Tools for AI:

Apache Spark

Hadoop


12. Cloud Platforms for AI Deployment:

Google Cloud AI

AWS SageMaker

Microsoft Azure AI


13. Popular AI APIs and Services:

Google Cloud Vision API

Microsoft Azure Cognitive Services

IBM Watson AI APIs


14. Learning Resources and Communities:

Kaggle

GitHub AI Projects

Papers with Code


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

ENJOY LEARNING 👍👍

Many data scientists don't know how to push ML models to production. Here's the recipe 👇

𝗞𝗲𝘆 𝗜𝗻𝗴𝗿𝗲𝗱𝗶𝗲𝗻𝘁𝘀

🔹 𝗧𝗿𝗮𝗶𝗻 / 𝗧𝗲𝘀𝘁 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 - Ensure Test is representative of Online data
🔹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 - Generate features in real-time
🔹 𝗠𝗼𝗱𝗲𝗹 𝗢𝗯𝗷𝗲𝗰𝘁 - Trained SkLearn or Tensorflow Model
🔹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗖𝗼𝗱𝗲 𝗥𝗲𝗽𝗼 - Save model project code to Github
🔹 𝗔𝗣𝗜 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 - Use FastAPI or Flask to build a model API
🔹 𝗗𝗼𝗰𝗸𝗲𝗿 - Containerize the ML model API
🔹 𝗥𝗲𝗺𝗼𝘁𝗲 𝗦𝗲𝗿𝘃𝗲𝗿 - Choose a cloud service; e.g. AWS sagemaker
🔹 𝗨𝗻𝗶𝘁 𝗧𝗲𝘀𝘁𝘀 - Test inputs & outputs of functions and APIs
🔹 𝗠𝗼𝗱𝗲𝗹 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 - Evidently AI, a simple, open-source for ML monitoring

𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲

𝗦𝘁𝗲𝗽 𝟭 - 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴

Don't push a model with 90% accuracy on train set. Do it based on the test set - if and only if, the test set is representative of the online data. Use SkLearn pipeline to chain a series of model preprocessing functions like null handling.

𝗦𝘁𝗲𝗽 𝟮 - 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁

Train your model with frameworks like Sklearn or Tensorflow. Push the model code including preprocessing, training and validation noscripts to Github for reproducibility.

𝗦𝘁𝗲𝗽 𝟯 - 𝗔𝗣𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 & 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻

Your model needs a "/predict" endpoint, which receives a JSON object in the request input and generates a JSON object with the model score in the response output. You can use frameworks like FastAPI or Flask. Containzerize this API so that it's agnostic to server environment

𝗦𝘁𝗲𝗽 𝟰 - 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁

Write tests to validate inputs & outputs of API functions to prevent errors. Push the code to remote services like AWS Sagemaker.

𝗦𝘁𝗲𝗽 𝟱 - 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴

Set up monitoring tools like Evidently AI, or use a built-in one within AWS Sagemaker. I use such tools to track performance metrics and data drifts on online data.

Data Science Resources
👇👇
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

Like if you need similar content 😄👍

Useful WhatsApp channels to learn AI Tools 🤖

ChatGPT: https://whatsapp.com/channel/0029VapThS265yDAfwe97c23

OpenAI: https://whatsapp.com/channel/0029VbAbfqcLtOj7Zen5tt3o

Deepseek: https://whatsapp.com/channel/0029Vb9js9sGpLHJGIvX5g1w

Perplexity AI: https://whatsapp.com/channel/0029VbAa05yISTkGgBqyC00U

Copilot: https://whatsapp.com/channel/0029VbAW0QBDOQIgYcbwBd1l

Generative AI: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U

Prompt Engineering: https://whatsapp.com/channel/0029Vb6ISO1Fsn0kEemhE03b

Artificial Intelligence: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E

Grok AI: https://whatsapp.com/channel/0029VbAU3pWChq6T5bZxUk1r

Deeplearning AI: https://whatsapp.com/channel/0029VbAKiI1FSAt81kV3lA0t

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

React ❤️ for more

✅ 5 Things You Can Build with Generative AI (Even as a Beginner) 🚀🤖

1️⃣ AI Blog Writer ✍️
➤ Use LLMs like GPT to generate blog posts on any topic.
➤ Tools: ChatGPT, Notion AI
➤ Monetize via affiliate links or ads.

2️⃣ AI-Powered Instagram Page 📸
➤ Generate quotes, carousels, or art using text-to-image models.
➤ Tools: DALL·E, Canva + GPT
➤ Grow followers & earn via brand deals.

3️⃣ Custom Chatbot for Business 🛍️
➤ Train a GPT bot to answer customer queries or handle bookings.
➤ Tools: GPT API, Botpress
➤ Sell as a service to small businesses.

4️⃣ Auto Code Assistant 💻
➤ Build a tool that helps write or explain code using AI.
➤ Tools: GitHub Copilot, GPT-4
➤ Great for developers & SaaS builders.

5️⃣ AI Video Shorts Generator 🎞️
➤ Convert blog posts or noscripts into short videos using AI.
➤ Tools: Runway, Pika Labs, ElevenLabs
➤ Monetize via YouTube, IG Reels, or clients.

💡 Combine multiple AI tools for even more powerful results.

💬 Tap ❤️ for more!

AI Agent GuideBook 🚀

React ❤️ For More

🚀 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 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 :)

⬇️ 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 Denoscription
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.me/datasciencefun