This is how ML works

Guys, Big Announcement!

We’ve officially hit 2 MILLION followers — and it’s time to take our Python journey to the next level!

I’m super excited to launch the 30-Day Python Coding Challenge — perfect for absolute beginners, interview prep, or anyone wanting to build real projects from scratch.

This challenge is your daily dose of Python — bite-sized lessons with hands-on projects so you actually code every day and level up fast.

Here’s what you’ll learn over the next 30 days:

Week 1: Python Fundamentals

- Variables & Data Types (Build your own bio/profile noscript)

- Operators (Mini calculator to sharpen math skills)

- Strings & String Methods (Word counter & palindrome checker)

- Lists & Tuples (Manage a grocery list like a pro)

- Dictionaries & Sets (Create your own contact book)

- Conditionals (Make a guess-the-number game)

- Loops (Multiplication tables & pattern printing)

Week 2: Functions & Logic — Make Your Code Smarter

- Functions (Prime number checker)

- Function Arguments (Tip calculator with custom tips)

- Recursion Basics (Factorials & Fibonacci series)

- Lambda, map & filter (Process lists efficiently)

- List Comprehensions (Filter odd/even numbers easily)

- Error Handling (Build a safe input reader)

- Review + Mini Project (Command-line to-do list)


Week 3: Files, Modules & OOP

- Reading & Writing Files (Save and load notes)

- Custom Modules (Create your own utility math module)

- Classes & Objects (Student grade tracker)

- Inheritance & OOP (RPG character system)

- Dunder Methods (Build a custom string class)

- OOP Mini Project (Simple bank account system)

- Review & Practice (Quiz app using OOP concepts)


Week 4: Real-World Python & APIs — Build Cool Apps

- JSON & APIs (Fetch weather data)

- Web Scraping (Extract noscripts from HTML)

- Regular Expressions (Find emails & phone numbers)

- Tkinter GUI (Create a simple counter app)

- CLI Tools (Command-line calculator with argparse)

- Automation (File organizer noscript)

- Final Project (Choose, build, and polish your app!)

React with ❤️ if you're ready for this new journey

You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1661

To start with Machine Learning:

1. Learn Python
2. Practice using Google Colab


Take these free courses:

https://news.1rj.ru/str/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://news.1rj.ru/str/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.✌️✌️

🐍 𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐞𝐥𝐭 𝐢𝐦𝐩𝐨𝐬𝐬𝐢𝐛𝐥𝐞 𝐚𝐭 𝐟𝐢𝐫𝐬𝐭, 𝐛𝐮𝐭 𝐭𝐡𝐞𝐬𝐞 𝟗 𝐬𝐭𝐞𝐩𝐬 𝐜𝐡𝐚𝐧𝐠𝐞𝐝 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠!
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1️⃣ 𝐌𝐚𝐬𝐭𝐞𝐫𝐞𝐝 𝐭𝐡𝐞 𝐁𝐚𝐬𝐢𝐜𝐬: Started with foundational Python concepts like variables, loops, functions, and conditional statements.

2️⃣ 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐝 𝐄𝐚𝐬𝐲 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬: Focused on beginner-friendly problems on platforms like LeetCode and HackerRank to build confidence.

3️⃣ 𝐅𝐨𝐥𝐥𝐨𝐰𝐞𝐝 𝐏𝐲𝐭𝐡𝐨𝐧-𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬: Studied essential problem-solving techniques for Python, like list comprehensions, dictionary manipulations, and lambda functions.

4️⃣ 𝐋𝐞𝐚𝐫𝐧𝐞𝐝 𝐊𝐞𝐲 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬: Explored popular libraries like Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization.

5️⃣ 𝐅𝐨𝐜𝐮𝐬𝐞𝐝 𝐨𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Built small projects like a to-do app, calculator, or data visualization dashboard to apply concepts.

6️⃣ 𝐖𝐚𝐭𝐜𝐡𝐞𝐝 𝐓𝐮𝐭𝐨𝐫𝐢𝐚𝐥𝐬: Followed creators like CodeWithHarry and Shradha Khapra for in-depth Python tutorials.

7️⃣ 𝐃𝐞𝐛𝐮𝐠𝐠𝐞𝐝 𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐥𝐲: Made it a habit to debug and analyze code to understand errors and optimize solutions.

8️⃣ 𝐉𝐨𝐢𝐧𝐞𝐝 𝐌𝐨𝐜𝐤 𝐂𝐨𝐝𝐢𝐧𝐠 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: Participated in coding challenges to simulate real-world problem-solving scenarios.

9️⃣ 𝐒𝐭𝐚𝐲𝐞𝐝 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭: Practiced daily, worked on diverse problems, and never skipped Python for more than a day.

I have curated the best interview resources to crack Python Interviews 👇👇
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

Hope you'll like it

Like this post if you need more resources like this 👍❤️

#Python

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

Python project-based interview questions for a data analyst role, along with tips and sample answers [Part-1]

1. Data Cleaning and Preprocessing
- Question: Can you walk me through the data cleaning process you followed in a Python-based project?
- Answer: In my project, I used Pandas for data manipulation. First, I handled missing values by imputing them with the median for numerical columns and the most frequent value for categorical columns using fillna(). I also removed outliers by setting a threshold based on the interquartile range (IQR). Additionally, I standardized numerical columns using StandardScaler from Scikit-learn and performed one-hot encoding for categorical variables using Pandas' get_dummies() function.
- Tip: Mention specific functions you used, like dropna(), fillna(), apply(), or replace(), and explain your rationale for selecting each method.

2. Exploratory Data Analysis (EDA)
- Question: How did you perform EDA in a Python project? What tools did you use?
- Answer: I used Pandas for data exploration, generating summary statistics with describe() and checking for correlations with corr(). For visualization, I used Matplotlib and Seaborn to create histograms, scatter plots, and box plots. For instance, I used sns.pairplot() to visually assess relationships between numerical features, which helped me detect potential multicollinearity. Additionally, I applied pivot tables to analyze key metrics by different categorical variables.
- Tip: Focus on how you used visualization tools like Matplotlib, Seaborn, or Plotly, and mention any specific insights you gained from EDA (e.g., data distributions, relationships, outliers).

3. Pandas Operations
- Question: Can you explain a situation where you had to manipulate a large dataset in Python using Pandas?
- Answer: In a project, I worked with a dataset containing over a million rows. I optimized my operations by using vectorized operations instead of Python loops. For example, I used apply() with a lambda function to transform a column, and groupby() to aggregate data by multiple dimensions efficiently. I also leveraged merge() to join datasets on common keys.
- Tip: Emphasize your understanding of efficient data manipulation with Pandas, mentioning functions like groupby(), merge(), concat(), or pivot().

4. Data Visualization
- Question: How do you create visualizations in Python to communicate insights from data?
- Answer: I primarily use Matplotlib and Seaborn for static plots and Plotly for interactive dashboards. For example, in one project, I used sns.heatmap() to visualize the correlation matrix and sns.barplot() for comparing categorical data. For time-series data, I used Matplotlib to create line plots that displayed trends over time. When presenting the results, I tailored visualizations to the audience, ensuring clarity and simplicity.
- Tip: Mention the specific plots you created and how you customized them (e.g., adding labels, noscripts, adjusting axis scales). Highlight the importance of clear communication through visualization.

Like this post if you want next part of this interview series 👍❤️

Here you can find essential Python Interview Resources👇
https://news.1rj.ru/str/DataSimplifier

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

Hope it helps :)

🚀 Roadmap to Master Python Programming 🔰

📂 Python Fundamentals
 ∟📂 Learn Syntax, Variables & Data Types
  ∟📂 Master Control Flow & Functions
   ∟📂 Practice with Simple Projects

📂 Intermediate Concepts
 ∟📂 Object-Oriented Programming (OOP)
  ∟📂 Work with Modules & Packages
   ∟📂 Understand Exception Handling & File I/O

📂 Data Structures & Algorithms
 ∟📂 Lists, Tuples, Dictionaries & Sets
  ∟📂 Algorithms & Problem Solving
   ∟📂 Master Recursion & Iteration

📂 Python Libraries & Tools
 ∟📂 Get Comfortable with Pip & Virtual Environments
  ∟📂 Learn NumPy & Pandas for Data Handling
   ∟📂 Explore Matplotlib & Seaborn for Visualization

📂 Web Development with Python
 ∟📂 Understand Flask & Django Frameworks
  ∟📂 Build RESTful APIs
   ∟📂 Integrate Front-End & Back-End

📂 Advanced Topics
 ∟📂 Concurrency: Threads & Asyncio
  ∟📂 Learn Testing with PyTest
   ∟📂 Dive into Design Patterns

📂 Projects & Real-World Applications
 ∟📂 Build Command-Line Tools & Scripts
  ∟📂 Contribute to Open-Source
   ∟📂 Showcase on GitHub & Portfolio

📂 Interview Preparation & Job Hunting
 ∟📂 Solve Python Coding Challenges
  ∟📂 Master Data Structures & Algorithms Interviews
   ∟📂 Network & Apply for Python Roles

✅️ Happy Coding

React "❤️" for More 👨‍💻

Python for Data Analysis: Must-Know Libraries 👇👇

Python is one of the most powerful tools for Data Analysts, and these libraries will supercharge your data analysis workflow by helping you clean, manipulate, and visualize data efficiently.

🔥 Essential Python Libraries for Data Analysis:

✅ Pandas – The go-to library for data manipulation. It helps in filtering, grouping, merging datasets, handling missing values, and transforming data into a structured format.

📌 Example: Loading a CSV file and displaying the first 5 rows:

import pandas as pd df = pd.read_csv('data.csv') print(df.head()) 

✅ NumPy – Used for handling numerical data and performing complex calculations. It provides support for multi-dimensional arrays and efficient mathematical operations.

📌 Example: Creating an array and performing basic operations:

import numpy as np arr = np.array([10, 20, 30]) print(arr.mean()) # Calculates the average 

✅ Matplotlib & Seaborn – These are used for creating visualizations like line graphs, bar charts, and scatter plots to understand trends and patterns in data.

📌 Example: Creating a basic bar chart:

import matplotlib.pyplot as plt plt.bar(['A', 'B', 'C'], [5, 7, 3]) plt.show() 

✅ Scikit-Learn – A must-learn library if you want to apply machine learning techniques like regression, classification, and clustering on your dataset.

✅ OpenPyXL – Helps in automating Excel reports using Python by reading, writing, and modifying Excel files.

💡 Challenge for You!
Try writing a Python noscript that:
1️⃣ Reads a CSV file
2️⃣ Cleans missing data
3️⃣ Creates a simple visualization

React with ♥️ if you want me to post the noscript for above challenge! ⬇️

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

Hope it helps :)

🤖 AI/ML Roadmap

1️⃣ Math & Stats 🧮🔢: Learn Linear Algebra, Probability, and Calculus.
2️⃣ Programming 🐍💻: Master Python, NumPy, Pandas, and Matplotlib.
3️⃣ Machine Learning 📈🤖: Study Supervised & Unsupervised Learning, and Model Evaluation.
4️⃣ Deep Learning 🔥🧠: Understand Neural Networks, CNNs, RNNs, and Transformers.
5️⃣ Specializations 🎓🔬: Choose from NLP, Computer Vision, or Reinforcement Learning.
6️⃣ Big Data & Cloud ☁️📡: Work with SQL, NoSQL, AWS, and GCP.
7️⃣ MLOps & Deployment 🚀🛠️: Learn Flask, Docker, and Kubernetes.
8️⃣ Ethics & Safety ⚖️🛡️: Understand Bias, Fairness, and Explainability.
9️⃣ Research & Practice 📜🔍: Read Papers and Build Projects.
🔟 Projects 📂🚀: Compete in Kaggle and contribute to Open-Source.

React ❤️ for more

#ai

Top 5 Regression Algorithms in ML

Free Datasets to practice data science projects

1. Enron Email Dataset

Data Link: https://www.cs.cmu.edu/~enron/

2. Chatbot Intents Dataset

Data Link: https://github.com/katanaml/katana-assistant/blob/master/mlbackend/intents.json

3. Flickr 30k Dataset

Data Link: https://www.kaggle.com/hsankesara/flickr-image-dataset

4. Parkinson Dataset

Data Link: https://archive.ics.uci.edu/ml/datasets/parkinsons

5. Iris Dataset

Data Link: https://archive.ics.uci.edu/ml/datasets/Iris

6. ImageNet dataset

Data Link: http://www.image-net.org/

7. Mall Customers Dataset

Data Link: https://www.kaggle.com/shwetabh123/mall-customers

8. Google Trends Data Portal

Data Link: https://trends.google.com/trends/

9. The Boston Housing Dataset

Data Link: https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html

10. Uber Pickups Dataset

Data Link: https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city

11. Recommender Systems Dataset

Data Link: https://cseweb.ucsd.edu/~jmcauley/datasets.html

Source Code: https://bit.ly/37iBDEp

12. UCI Spambase Dataset

Data Link: https://archive.ics.uci.edu/ml/datasets/Spambase

13. GTSRB (German traffic sign recognition benchmark) Dataset

Data Link: http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset

Source Code: https://bit.ly/39taSyH

14. Cityscapes Dataset

Data Link: https://www.cityscapes-dataset.com/

15. Kinetics Dataset

Data Link: https://deepmind.com/research/open-source/kinetics

16. IMDB-Wiki dataset

Data Link: https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/


17. Color Detection Dataset

Data Link: https://github.com/codebrainz/color-names/blob/master/output/colors.csv


18. Urban Sound 8K dataset

Data Link: https://urbansounddataset.weebly.com/urbansound8k.html

19. Librispeech Dataset

Data Link: http://www.openslr.org/12

20. Breast Histopathology Images Dataset

Data Link: https://www.kaggle.com/paultimothymooney/breast-histopathology-images

21. Youtube 8M Dataset

Data Link: https://research.google.com/youtube8m/

Join for more -> https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z

ENJOY LEARNING 👍👍

Source codes for data science projects 👇👇

1. Build chatbots:
https://dzone.com/articles/python-chatbot-project-build-your-first-python-pro

2. Credit card fraud detection:
https://www.kaggle.com/renjithmadhavan/credit-card-fraud-detection-using-python

3. Fake news detection
https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/

4.Driver Drowsiness Detection
https://data-flair.training/blogs/python-project-driver-drowsiness-detection-system/

5. Recommender Systems (Movie Recommendation)
https://data-flair.training/blogs/data-science-r-movie-recommendation/

6. Sentiment Analysis
https://data-flair.training/blogs/data-science-r-sentiment-analysis-project/

7. Gender Detection & Age Prediction
https://www.pyimagesearch.com/2020/04/13/opencv-age-detection-with-deep-learning/

𝗘𝗡𝗝𝗢𝗬 𝗟𝗘𝗔𝗥𝗡𝗜𝗡𝗚👍👍

🚀 Key Skills for Aspiring Tech Specialists

📊 Data Analyst:
- Proficiency in SQL for database querying
- Advanced Excel for data manipulation
- Programming with Python or R for data analysis
- Statistical analysis to understand data trends
- Data visualization tools like Tableau or PowerBI
- Data preprocessing to clean and structure data
- Exploratory data analysis techniques

🧠 Data Scientist:
- Strong knowledge of Python and R for statistical analysis
- Machine learning for predictive modeling
- Deep understanding of mathematics and statistics
- Data wrangling to prepare data for analysis
- Big data platforms like Hadoop or Spark
- Data visualization and communication skills
- Experience with A/B testing frameworks

🏗 Data Engineer:
- Expertise in SQL and NoSQL databases
- Experience with data warehousing solutions
- ETL (Extract, Transform, Load) process knowledge
- Familiarity with big data tools (e.g., Apache Spark)
- Proficient in Python, Java, or Scala
- Knowledge of cloud services like AWS, GCP, or Azure
- Understanding of data pipeline and workflow management tools

🤖 Machine Learning Engineer:
- Proficiency in Python and libraries like scikit-learn, TensorFlow
- Solid understanding of machine learning algorithms
- Experience with neural networks and deep learning frameworks
- Ability to implement models and fine-tune their parameters
- Knowledge of software engineering best practices
- Data modeling and evaluation strategies
- Strong mathematical skills, particularly in linear algebra and calculus

🧠 Deep Learning Engineer:
- Expertise in deep learning frameworks like TensorFlow or PyTorch
- Understanding of Convolutional and Recurrent Neural Networks
- Experience with GPU computing and parallel processing
- Familiarity with computer vision and natural language processing
- Ability to handle large datasets and train complex models
- Research mindset to keep up with the latest developments in deep learning

🤯 AI Engineer:
- Solid foundation in algorithms, logic, and mathematics
- Proficiency in programming languages like Python or C++
- Experience with AI technologies including ML, neural networks, and cognitive computing
- Understanding of AI model deployment and scaling
- Knowledge of AI ethics and responsible AI practices
- Strong problem-solving and analytical skills

🔊 NLP Engineer:
- Background in linguistics and language models
- Proficiency with NLP libraries (e.g., NLTK, spaCy)
- Experience with text preprocessing and tokenization
- Understanding of sentiment analysis, text classification, and named entity recognition
- Familiarity with transformer models like BERT and GPT
- Ability to work with large text datasets and sequential data

🌟 Embrace the world of data and AI, and become the architect of tomorrow's technology!