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
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
❤11😁1
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
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
❤2
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 🙌
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 🙌
❤9
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:
✅ 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:
✅ 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:
✅ 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 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 :)
❤5🔥4
Machine Learning – Essential Concepts 🚀
1️⃣ Types of Machine Learning
Supervised Learning – Uses labeled data to train models.
Examples: Linear Regression, Decision Trees, Random Forest, SVM
Unsupervised Learning – Identifies patterns in unlabeled data.
Examples: Clustering (K-Means, DBSCAN), PCA
Reinforcement Learning – Models learn through rewards and penalties.
Examples: Q-Learning, Deep Q Networks
2️⃣ Key Algorithms
Regression – Predicts continuous values (Linear Regression, Ridge, Lasso).
Classification – Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naïve Bayes).
Clustering – Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).
Dimensionality Reduction – Reduces the number of features (PCA, t-SNE, LDA).
3️⃣ Model Training & Evaluation
Train-Test Split – Dividing data into training and testing sets.
Cross-Validation – Splitting data multiple times for better accuracy.
Metrics – Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.
4️⃣ Feature Engineering
Handling missing data (mean imputation, dropna()).
Encoding categorical variables (One-Hot Encoding, Label Encoding).
Feature Scaling (Normalization, Standardization).
5️⃣ Overfitting & Underfitting
Overfitting – Model learns noise, performs well on training but poorly on test data.
Underfitting – Model is too simple and fails to capture patterns.
Solution: Regularization (L1, L2), Hyperparameter Tuning.
6️⃣ Ensemble Learning
Combining multiple models to improve performance.
Bagging (Random Forest)
Boosting (XGBoost, Gradient Boosting, AdaBoost)
7️⃣ Deep Learning Basics
Neural Networks (ANN, CNN, RNN).
Activation Functions (ReLU, Sigmoid, Tanh).
Backpropagation & Gradient Descent.
8️⃣ Model Deployment
Deploy models using Flask, FastAPI, or Streamlit.
Model versioning with MLflow.
Cloud deployment (AWS SageMaker, Google Vertex AI).
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
1️⃣ Types of Machine Learning
Supervised Learning – Uses labeled data to train models.
Examples: Linear Regression, Decision Trees, Random Forest, SVM
Unsupervised Learning – Identifies patterns in unlabeled data.
Examples: Clustering (K-Means, DBSCAN), PCA
Reinforcement Learning – Models learn through rewards and penalties.
Examples: Q-Learning, Deep Q Networks
2️⃣ Key Algorithms
Regression – Predicts continuous values (Linear Regression, Ridge, Lasso).
Classification – Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naïve Bayes).
Clustering – Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).
Dimensionality Reduction – Reduces the number of features (PCA, t-SNE, LDA).
3️⃣ Model Training & Evaluation
Train-Test Split – Dividing data into training and testing sets.
Cross-Validation – Splitting data multiple times for better accuracy.
Metrics – Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.
4️⃣ Feature Engineering
Handling missing data (mean imputation, dropna()).
Encoding categorical variables (One-Hot Encoding, Label Encoding).
Feature Scaling (Normalization, Standardization).
5️⃣ Overfitting & Underfitting
Overfitting – Model learns noise, performs well on training but poorly on test data.
Underfitting – Model is too simple and fails to capture patterns.
Solution: Regularization (L1, L2), Hyperparameter Tuning.
6️⃣ Ensemble Learning
Combining multiple models to improve performance.
Bagging (Random Forest)
Boosting (XGBoost, Gradient Boosting, AdaBoost)
7️⃣ Deep Learning Basics
Neural Networks (ANN, CNN, RNN).
Activation Functions (ReLU, Sigmoid, Tanh).
Backpropagation & Gradient Descent.
8️⃣ Model Deployment
Deploy models using Flask, FastAPI, or Streamlit.
Model versioning with MLflow.
Cloud deployment (AWS SageMaker, Google Vertex AI).
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
❤6
Best YouTube Playlists for Data Science
▶️ Python
🔗 Playlist Link
▶️ SQL
🔗 Playlist Link
▶️ Data Analysis
🔗 Playlist Link
▶️ Data Analyst
🔗 Playlist Link
▶️ Linear Algebra
🔗 Playlist Link
▶️ Calculus
🔗 Playlist Link
▶️ Statistics
🔗 Playlist Link
▶️ Machine Learning
🔗 Playlist Link
▶️ Deep Learning
🔗 Playlist Link
▶️ Excel Power Query
🔗 Playlist Link
▶️ Ruby
🔗 Playlist Link
▶️ Microsoft Excel
🔗 Playlist Link
▶️ Python
🔗 Playlist Link
▶️ SQL
🔗 Playlist Link
▶️ Data Analysis
🔗 Playlist Link
▶️ Data Analyst
🔗 Playlist Link
▶️ Linear Algebra
🔗 Playlist Link
▶️ Calculus
🔗 Playlist Link
▶️ Statistics
🔗 Playlist Link
▶️ Machine Learning
🔗 Playlist Link
▶️ Deep Learning
🔗 Playlist Link
▶️ Excel Power Query
🔗 Playlist Link
▶️ Ruby
🔗 Playlist Link
▶️ Microsoft Excel
🔗 Playlist Link
❤12🔥2
10 Ways to Speed Up Your Python Code
1. List Comprehensions
numbers = [x**2 for x in range(100000) if x % 2 == 0]
instead of
numbers = []
for x in range(100000):
if x % 2 == 0:
numbers.append(x**2)
2. Use the Built-In Functions
Many of Python’s built-in functions are written in C, which makes them much faster than a pure python solution.
3. Function Calls Are Expensive
Function calls are expensive in Python. While it is often good practice to separate code into functions, there are times where you should be cautious about calling functions from inside of a loop. It is better to iterate inside a function than to iterate and call a function each iteration.
4. Lazy Module Importing
If you want to use the time.sleep() function in your code, you don't necessarily need to import the entire time package. Instead, you can just do from time import sleep and avoid the overhead of loading basically everything.
5. Take Advantage of Numpy
Numpy is a highly optimized library built with C. It is almost always faster to offload complex math to Numpy rather than relying on the Python interpreter.
6. Try Multiprocessing
Multiprocessing can bring large performance increases to a Python noscript, but it can be difficult to implement properly compared to other methods mentioned in this post.
7. Be Careful with Bulky Libraries
One of the advantages Python has over other programming languages is the rich selection of third-party libraries available to developers. But, what we may not always consider is the size of the library we are using as a dependency, which could actually decrease the performance of your Python code.
8. Avoid Global Variables
Python is slightly faster at retrieving local variables than global ones. It is simply best to avoid global variables when possible.
9. Try Multiple Solutions
Being able to solve a problem in multiple ways is nice. But, there is often a solution that is faster than the rest and sometimes it comes down to just using a different method or data structure.
10. Think About Your Data Structures
Searching a dictionary or set is insanely fast, but lists take time proportional to the length of the list. However, sets and dictionaries do not maintain order. If you care about the order of your data, you can’t make use of dictionaries or sets.
1. List Comprehensions
numbers = [x**2 for x in range(100000) if x % 2 == 0]
instead of
numbers = []
for x in range(100000):
if x % 2 == 0:
numbers.append(x**2)
2. Use the Built-In Functions
Many of Python’s built-in functions are written in C, which makes them much faster than a pure python solution.
3. Function Calls Are Expensive
Function calls are expensive in Python. While it is often good practice to separate code into functions, there are times where you should be cautious about calling functions from inside of a loop. It is better to iterate inside a function than to iterate and call a function each iteration.
4. Lazy Module Importing
If you want to use the time.sleep() function in your code, you don't necessarily need to import the entire time package. Instead, you can just do from time import sleep and avoid the overhead of loading basically everything.
5. Take Advantage of Numpy
Numpy is a highly optimized library built with C. It is almost always faster to offload complex math to Numpy rather than relying on the Python interpreter.
6. Try Multiprocessing
Multiprocessing can bring large performance increases to a Python noscript, but it can be difficult to implement properly compared to other methods mentioned in this post.
7. Be Careful with Bulky Libraries
One of the advantages Python has over other programming languages is the rich selection of third-party libraries available to developers. But, what we may not always consider is the size of the library we are using as a dependency, which could actually decrease the performance of your Python code.
8. Avoid Global Variables
Python is slightly faster at retrieving local variables than global ones. It is simply best to avoid global variables when possible.
9. Try Multiple Solutions
Being able to solve a problem in multiple ways is nice. But, there is often a solution that is faster than the rest and sometimes it comes down to just using a different method or data structure.
10. Think About Your Data Structures
Searching a dictionary or set is insanely fast, but lists take time proportional to the length of the list. However, sets and dictionaries do not maintain order. If you care about the order of your data, you can’t make use of dictionaries or sets.
❤9