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

Machine Learning isn't easy!

It’s the field that powers intelligent systems and predictive models.

To truly master Machine Learning, focus on these key areas:

0. Understanding the Basics of Algorithms: Learn about linear regression, decision trees, and k-nearest neighbors to build a solid foundation.


1. Mastering Data Preprocessing: Clean, normalize, and handle missing data to prepare your datasets for training.


2. Learning Supervised Learning Techniques: Dive deep into classification and regression models, such as SVMs, random forests, and logistic regression.


3. Exploring Unsupervised Learning: Understand clustering techniques (K-means, hierarchical) and dimensionality reduction (PCA, t-SNE).


4. Mastering Model Evaluation: Use techniques like cross-validation, confusion matrices, ROC curves, and F1 scores to assess model performance.


5. Understanding Overfitting and Underfitting: Learn how to balance bias and variance to build robust models.


6. Optimizing Hyperparameters: Use grid search, random search, and Bayesian optimization to fine-tune your models for better performance.


7. Diving into Neural Networks and Deep Learning: Explore deep learning with frameworks like TensorFlow and PyTorch to create advanced models like CNNs and RNNs.


8. Working with Natural Language Processing (NLP): Master text data, sentiment analysis, and techniques like word embeddings and transformers.


9. Staying Updated with New Techniques: Machine learning evolves rapidly—keep up with emerging models, techniques, and research.



Machine learning is about learning from data and improving models over time.

💡 Embrace the challenges of building algorithms, experimenting with data, and solving complex problems.

⏳ With time, practice, and persistence, you’ll develop the expertise to create systems that learn, predict, and adapt.

Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://news.1rj.ru/str/datasciencefun

Like if you need similar content 😄👍

Hope this helps you 😊

#datascience

𝗠𝗼𝘀𝘁 𝗔𝘀𝗸𝗲𝗱 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗮𝘁 𝗠𝗔𝗔𝗡𝗚 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀🔥🔥

1. How do you retrieve all columns from a table?

SELECT * FROM table_name;


2. What SQL statement is used to filter records?

SELECT * FROM table_name
WHERE condition;

The WHERE clause is used to filter records based on a specified condition.


3. How can you join multiple tables? Describe different types of JOINs.

SELECT columns
FROM table1
JOIN table2 ON table1.column = table2.column
JOIN table3 ON table2.column = table3.column;

Types of JOINs:

1. INNER JOIN: Returns records with matching values in both tables

SELECT * FROM table1
INNER JOIN table2 ON table1.column = table2.column;

2. LEFT JOIN (or LEFT OUTER JOIN): Returns all records from the left table and matched records from the right table. Unmatched records will have NULL values.

SELECT * FROM table1
LEFT JOIN table2 ON table1.column = table2.column;

3. RIGHT JOIN (or RIGHT OUTER JOIN): Returns all records from the right table and matched records from the left table. Unmatched records will have NULL values.

SELECT * FROM table1
RIGHT JOIN table2 ON table1.column = table2.column;

4. FULL JOIN (or FULL OUTER JOIN): Returns records when there is a match in either left or right table. Unmatched records will have NULL values.

SELECT * FROM table1
FULL JOIN table2 ON table1.column = table2.column;


4. What is the difference between WHERE and HAVING clauses?

WHERE: Filters records before any groupings are made.

SELECT * FROM table_name
WHERE condition;

HAVING: Filters records after groupings are made.

SELECT column, COUNT(*)
FROM table_name
GROUP BY column
HAVING COUNT(*) > value;


5. How do you count the number of records in a table?

SELECT COUNT(*) FROM table_name;

This query counts all the records in the specified table.

6. How do you calculate average, sum, minimum, and maximum values in a column?

Average: SELECT AVG(column_name) FROM table_name;

Sum: SELECT SUM(column_name) FROM table_name;

Minimum: SELECT MIN(column_name) FROM table_name;

Maximum: SELECT MAX(column_name) FROM table_name;


7. What is a subquery, and how do you use it?

Subquery: A query nested inside another query

SELECT * FROM table_name
WHERE column_name = (SELECT column_name FROM another_table WHERE condition);




Till then keep learning and keep exploring 🙌

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 :)

Python 💪❤️

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