Guys, Big Announcement!
We’ve officially hit 5 Lakh followers on WhatsApp and it’s time to level up together! ❤️
I've launched a Python Learning Series — designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step journey — from basics to advanced — with real examples and short quizzes after each topic to help you lock in the concepts.
Here’s what we’ll cover in the coming days:
Week 1: Python Fundamentals
- Variables & Data Types
- Operators & Expressions
- Conditional Statements (if, elif, else)
- Loops (for, while)
- Functions & Parameters
- Input/Output & Basic Formatting
Week 2: Core Python Skills
- Lists, Tuples, Sets, Dictionaries
- String Manipulation
- List Comprehensions
- File Handling
- Exception Handling
Week 3: Intermediate Python
- Lambda Functions
- Map, Filter, Reduce
- Modules & Packages
- Scope & Global Variables
- Working with Dates & Time
Week 4: OOP & Pythonic Concepts
- Classes & Objects
- Inheritance & Polymorphism
- Decorators (Intro level)
- Generators & Iterators
- Writing Clean & Readable Code
Week 5: Real-World & Interview Prep
- Web Scraping (BeautifulSoup)
- Working with APIs (Requests)
- Automating Tasks
- Data Analysis Basics (Pandas)
- Interview Coding Patterns
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1527
We’ve officially hit 5 Lakh followers on WhatsApp and it’s time to level up together! ❤️
I've launched a Python Learning Series — designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step journey — from basics to advanced — with real examples and short quizzes after each topic to help you lock in the concepts.
Here’s what we’ll cover in the coming days:
Week 1: Python Fundamentals
- Variables & Data Types
- Operators & Expressions
- Conditional Statements (if, elif, else)
- Loops (for, while)
- Functions & Parameters
- Input/Output & Basic Formatting
Week 2: Core Python Skills
- Lists, Tuples, Sets, Dictionaries
- String Manipulation
- List Comprehensions
- File Handling
- Exception Handling
Week 3: Intermediate Python
- Lambda Functions
- Map, Filter, Reduce
- Modules & Packages
- Scope & Global Variables
- Working with Dates & Time
Week 4: OOP & Pythonic Concepts
- Classes & Objects
- Inheritance & Polymorphism
- Decorators (Intro level)
- Generators & Iterators
- Writing Clean & Readable Code
Week 5: Real-World & Interview Prep
- Web Scraping (BeautifulSoup)
- Working with APIs (Requests)
- Automating Tasks
- Data Analysis Basics (Pandas)
- Interview Coding Patterns
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1527
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🚀 𝗛𝗼𝘄 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗧𝗵𝗮𝘁 𝗧𝗿𝘂𝗹𝘆 𝗦𝘁𝗮𝗻𝗱𝘀 𝗢𝘂𝘁
In today’s competitive landscape, a strong resume alone won't get you far. If you're aiming for 𝘆𝗼𝘂𝗿 𝗱𝗿𝗲𝗮𝗺 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗿𝗼𝗹𝗲, you need a portfolio that speaks volumes—one that highlights your skills, thinking process, and real-world impact.
A great portfolio isn’t just a collection of projects. It’s your story as a data scientist—and here’s how to make it unforgettable:
🔹 𝗪𝗵𝗮𝘁 𝗠𝗮𝗸𝗲𝘀 𝗮𝗻 𝗘𝘅𝗰𝗲𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼?
✅ Quality Over Quantity – A few impactful projects are far better than a dozen generic ones.
✅ Tell a Story – Clearly explain the problem, your approach, and key insights. Keep it engaging.
✅ Show Range – Demonstrate a variety of skills—data cleaning, visualization, analytics, modeling.
✅ Make It Relevant – Choose projects with real-world business value, not just toy Kaggle datasets.
🔥 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗜𝗱𝗲𝗮𝘀 𝗧𝗵𝗮𝘁 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿𝘀 𝗡𝗼𝘁𝗶𝗰𝗲
1️⃣ Customer Churn Prediction – Help businesses retain customers through insights.
2️⃣ Social Media Sentiment Analysis – Extract opinions from real-time data like tweets or reviews.
3️⃣ Supply Chain Optimization – Solve efficiency problems using operational data.
4️⃣ E-commerce Recommender System – Personalize shopping experiences with smart suggestions.
5️⃣ Interactive Dashboards – Use Power BI or Tableau to tell compelling visual stories.
📌 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗳𝗼𝗿 𝗮 𝗞𝗶𝗹𝗹𝗲𝗿 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼
💡 Host on GitHub – Keep your code clean, well-structured, and documented.
💡 Write About It – Use Medium or your own site to explain your projects and decisions.
💡 Deploy Your Work – Use tools like Streamlit, Flask, or FastAPI to make your projects interactive.
💡 Open Source Contributions – It’s a great way to gain credibility and connect with others.
A great data science portfolio is not just about code—it's about solving real problems with data.
Free Data Science Resources: https://t.iss.one/datalemur
All the best 👍👍
In today’s competitive landscape, a strong resume alone won't get you far. If you're aiming for 𝘆𝗼𝘂𝗿 𝗱𝗿𝗲𝗮𝗺 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗿𝗼𝗹𝗲, you need a portfolio that speaks volumes—one that highlights your skills, thinking process, and real-world impact.
A great portfolio isn’t just a collection of projects. It’s your story as a data scientist—and here’s how to make it unforgettable:
🔹 𝗪𝗵𝗮𝘁 𝗠𝗮𝗸𝗲𝘀 𝗮𝗻 𝗘𝘅𝗰𝗲𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼?
✅ Quality Over Quantity – A few impactful projects are far better than a dozen generic ones.
✅ Tell a Story – Clearly explain the problem, your approach, and key insights. Keep it engaging.
✅ Show Range – Demonstrate a variety of skills—data cleaning, visualization, analytics, modeling.
✅ Make It Relevant – Choose projects with real-world business value, not just toy Kaggle datasets.
🔥 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗜𝗱𝗲𝗮𝘀 𝗧𝗵𝗮𝘁 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿𝘀 𝗡𝗼𝘁𝗶𝗰𝗲
1️⃣ Customer Churn Prediction – Help businesses retain customers through insights.
2️⃣ Social Media Sentiment Analysis – Extract opinions from real-time data like tweets or reviews.
3️⃣ Supply Chain Optimization – Solve efficiency problems using operational data.
4️⃣ E-commerce Recommender System – Personalize shopping experiences with smart suggestions.
5️⃣ Interactive Dashboards – Use Power BI or Tableau to tell compelling visual stories.
📌 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗳𝗼𝗿 𝗮 𝗞𝗶𝗹𝗹𝗲𝗿 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼
💡 Host on GitHub – Keep your code clean, well-structured, and documented.
💡 Write About It – Use Medium or your own site to explain your projects and decisions.
💡 Deploy Your Work – Use tools like Streamlit, Flask, or FastAPI to make your projects interactive.
💡 Open Source Contributions – It’s a great way to gain credibility and connect with others.
A great data science portfolio is not just about code—it's about solving real problems with data.
Free Data Science Resources: https://t.iss.one/datalemur
All the best 👍👍
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🖥 deepface - Python library for facial recognition and more
-
⏩ deepface is a lightweight Python library that allows you to find faces and analyze various attributes from photographs: age, gender, emotions.
It incorporates the best of the VGG-Face, FaceNet, OpenFace, DeepFace, DeepID, ArcFace, Dlib, SFace and GhostFaceNet models.
⏩ This is how you can compare the similarity of 2 faces, the result is in the image:
🖥 GitHub
〰️〰️〰️〰️〰️〰️〰️〰️〰️
-
pip install deepface⏩ deepface is a lightweight Python library that allows you to find faces and analyze various attributes from photographs: age, gender, emotions.
It incorporates the best of the VGG-Face, FaceNet, OpenFace, DeepFace, DeepID, ArcFace, Dlib, SFace and GhostFaceNet models.
⏩ This is how you can compare the similarity of 2 faces, the result is in the image:
from deepface import DeepFace
result = DeepFace.verify(img1_path = "img1.jpg", img2_path = "img2.jpg")
🖥 GitHub
〰️〰️〰️〰️〰️〰️〰️〰️〰️
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New Data Scientists - When you learn, it's easy to get distracted by Machine Learning & Deep Learning terms like "XGBoost", "Neural Networks", "RNN", "LSTM" or Advanced Technologies like "Spark", "Julia", "Scala", "Go", etc.
Don't get bogged down trying to learn every new term & technology you come across.
Instead, focus on foundations.
- data wrangling
- visualizing
- exploring
- modeling
- understanding the results.
The best tools are often basic, Build yourself up. You'll advance much faster. Keep learning!
Don't get bogged down trying to learn every new term & technology you come across.
Instead, focus on foundations.
- data wrangling
- visualizing
- exploring
- modeling
- understanding the results.
The best tools are often basic, Build yourself up. You'll advance much faster. Keep learning!
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5 misconceptions about data analytics (and what's actually true):
❌ The more sophisticated the tool, the better the analyst
✅ Many analysts do their jobs with "basic" tools like Excel
❌ You're just there to crunch the numbers
✅ You need to be able to tell a story with the data
❌ You need super advanced math skills
✅ Understanding basic math and statistics is a good place to start
❌ Data is always clean and accurate
✅ Data is never clean and 100% accurate (without lots of prep work)
❌ You'll work in isolation and not talk to anyone
✅ Communication with your team and your stakeholders is essential
❌ The more sophisticated the tool, the better the analyst
✅ Many analysts do their jobs with "basic" tools like Excel
❌ You're just there to crunch the numbers
✅ You need to be able to tell a story with the data
❌ You need super advanced math skills
✅ Understanding basic math and statistics is a good place to start
❌ Data is always clean and accurate
✅ Data is never clean and 100% accurate (without lots of prep work)
❌ You'll work in isolation and not talk to anyone
✅ Communication with your team and your stakeholders is essential
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Don't stress too much on which tools to learn first.
Pickup 2-3 tools and master them. Skills are transferable.
For eg- If you can create an amazing dashboard in Power BI, you can make similar impressive dashboard in Tableau as well.
If you can run efficient queries in MySQL, it's going to be nearly same in PostgreSQL as well.
If you can manipulate fields in Excel, you can do the same stuff in Google Sheets as well.
Continuity is the key 😄
Never stop Learning ❤️
Pickup 2-3 tools and master them. Skills are transferable.
For eg- If you can create an amazing dashboard in Power BI, you can make similar impressive dashboard in Tableau as well.
If you can run efficient queries in MySQL, it's going to be nearly same in PostgreSQL as well.
If you can manipulate fields in Excel, you can do the same stuff in Google Sheets as well.
Continuity is the key 😄
Never stop Learning ❤️
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🔰 Data Science Roadmap for Beginners 2025
├── 📘 What is Data Science?
├── 🧠 Data Science vs Data Analytics vs Machine Learning
├── 🛠 Tools of the Trade (Python, R, Excel, SQL)
├── 🐍 Python for Data Science (NumPy, Pandas, Matplotlib)
├── 🔢 Statistics & Probability Basics
├── 📊 Data Visualization (Matplotlib, Seaborn, Plotly)
├── 🧼 Data Cleaning & Preprocessing
├── 🧮 Exploratory Data Analysis (EDA)
├── 🧠 Introduction to Machine Learning
├── 📦 Supervised vs Unsupervised Learning
├── 🤖 Popular ML Algorithms (Linear Reg, KNN, Decision Trees)
├── 🧪 Model Evaluation (Accuracy, Precision, Recall, F1 Score)
├── 🧰 Model Tuning (Cross Validation, Grid Search)
├── ⚙️ Feature Engineering
├── 🏗 Real-world Projects (Kaggle, UCI Datasets)
├── 📈 Basic Deployment (Streamlit, Flask, Heroku)
├── 🔁 Continuous Learning: Blogs, Research Papers, Competitions
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like for more ❤️
├── 📘 What is Data Science?
├── 🧠 Data Science vs Data Analytics vs Machine Learning
├── 🛠 Tools of the Trade (Python, R, Excel, SQL)
├── 🐍 Python for Data Science (NumPy, Pandas, Matplotlib)
├── 🔢 Statistics & Probability Basics
├── 📊 Data Visualization (Matplotlib, Seaborn, Plotly)
├── 🧼 Data Cleaning & Preprocessing
├── 🧮 Exploratory Data Analysis (EDA)
├── 🧠 Introduction to Machine Learning
├── 📦 Supervised vs Unsupervised Learning
├── 🤖 Popular ML Algorithms (Linear Reg, KNN, Decision Trees)
├── 🧪 Model Evaluation (Accuracy, Precision, Recall, F1 Score)
├── 🧰 Model Tuning (Cross Validation, Grid Search)
├── ⚙️ Feature Engineering
├── 🏗 Real-world Projects (Kaggle, UCI Datasets)
├── 📈 Basic Deployment (Streamlit, Flask, Heroku)
├── 🔁 Continuous Learning: Blogs, Research Papers, Competitions
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like for more ❤️
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Yesterday we have posted free links to 9 courses from the most popular AI learning platforms on our WhatsApp channel.
These 9 courses covers LLMs, Agents, Deep RL, Audio and more
https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
These 9 courses covers LLMs, Agents, Deep RL, Audio and more
https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
WhatsApp.com
Artificial Intelligence | WhatsApp Channel
Artificial Intelligence WhatsApp Channel. 🔰 Machine Learning & Artificial Intelligence Free Resources
🔰 Learn Prompt Engineering with ChatGPT & Google Gemini
🔰 Advanced Data Science and Deep Learning Concepts
🔰 Build Chatbots & Large Language Models
🔰 Best…
🔰 Learn Prompt Engineering with ChatGPT & Google Gemini
🔰 Advanced Data Science and Deep Learning Concepts
🔰 Build Chatbots & Large Language Models
🔰 Best…
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To start with Machine Learning:
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://t.iss.one/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://t.iss.one/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or 𝕏 and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.✌️✌️
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://t.iss.one/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://t.iss.one/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or 𝕏 and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.✌️✌️
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🔥 Data Science Roadmap 2025
Step 1: 🐍 Python Basics
Step 2: 📊 Data Analysis (Pandas, NumPy)
Step 3: 📈 Data Visualization (Matplotlib, Seaborn)
Step 4: 🤖 Machine Learning (Scikit-learn)
Step 5: � Deep Learning (TensorFlow/PyTorch)
Step 6: 🗃️ SQL & Big Data (Spark)
Step 7: 🚀 Deploy Models (Flask, FastAPI)
Step 8: 📢 Showcase Projects
Step 9: 💼 Land a Job!
🔓 Pro Tip: Compete on Kaggle
#datascience
Step 1: 🐍 Python Basics
Step 2: 📊 Data Analysis (Pandas, NumPy)
Step 3: 📈 Data Visualization (Matplotlib, Seaborn)
Step 4: 🤖 Machine Learning (Scikit-learn)
Step 5: � Deep Learning (TensorFlow/PyTorch)
Step 6: 🗃️ SQL & Big Data (Spark)
Step 7: 🚀 Deploy Models (Flask, FastAPI)
Step 8: 📢 Showcase Projects
Step 9: 💼 Land a Job!
🔓 Pro Tip: Compete on Kaggle
#datascience
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