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پست‌های کانال
Deep Learning Cheat Sheet 🧑🏻💻
https://t.me/MachineLearning9 👾
| 2 | 📝 12 Essential Articles for Data Scientists
🏷 Article: Seq2Seq Learning with NN
https://arxiv.org/pdf/1409.3215
An introduction to Seq2Seq models, which serve as the foundation for machine translation utilizing deep learning.
🏷 Article: GANs
https://arxiv.org/pdf/1406.2661
An introduction to Generative Adversarial Networks (GANs) and the concept of generating synthetic data. This forms the basis for creating images and videos with artificial intelligence.
🏷 Article: Attention is All You Need
https://arxiv.org/pdf/1706.03762
This paper was revolutionary in natural language processing. It introduced the Transformer architecture, which underlies GPT, BERT, and contemporary intelligent language models.
🏷 Article: Deep Residual Learning
https://arxiv.org/pdf/1512.03385
This work introduced the ResNet model, enabling neural networks to achieve greater depth and accuracy without compromising the learning process.
🏷 Article: Batch Normalization
https://arxiv.org/pdf/1502.03167
This paper introduced a technique that facilitates faster and more stable training of neural networks.
🏷 Article: Dropout
https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf
A straightforward method designed to prevent overfitting in neural networks.
🏷 Article: ImageNet Classification with DCNN
https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
The first successful application of a deep neural network for image recognition.
🏷 Article: Support-Vector Machines
https://link.springer.com/content/pdf/10.1007/BF00994018.pdf
This seminal work introduced the Support Vector Machine (SVM) algorithm, a widely utilized method for data classification.
🏷 Article: A Few Useful Things to Know About ML
https://homes.cs.washington.edu/~pedro/papers/cacm12.pdf
A comprehensive collection of practical and empirical insights regarding machine learning.
🏷 Article: Gradient Boosting Machine
https://www.cse.iitb.ac.in/~soumen/readings/papers/Friedman1999GreedyFuncApprox.pdf
This paper introduced the "Gradient Boosting" method, which serves as the foundation for many modern machine learning models, including XGBoost and LightGBM.
🏷 Article: Latent Dirichlet Allocation
https://jmlr.org/papers/volume3/blei03a/blei03a.pdf
This work introduced a model for text analysis capable of identifying the topics discussed within an article.
🏷 Article: Random Forests
https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf
This paper introduced the "Random Forest" algorithm, a powerful machine learning method that aggregates multiple models to achieve enhanced accuracy. | 1 644 |
| 3 | Here are some essential data science concepts from A to Z:
A - Algorithm: A set of rules or instructions used to solve a problem or perform a task in data science.
B - Big Data: Large and complex datasets that cannot be easily processed using traditional data processing applications.
C - Clustering: A technique used to group similar data points together based on certain characteristics.
D - Data Cleaning: The process of identifying and correcting errors or inconsistencies in a dataset.
E - Exploratory Data Analysis (EDA): The process of analyzing and visualizing data to understand its underlying patterns and relationships.
F - Feature Engineering: The process of creating new features or variables from existing data to improve model performance.
G - Gradient Descent: An optimization algorithm used to minimize the error of a model by adjusting its parameters.
H - Hypothesis Testing: A statistical technique used to test the validity of a hypothesis or claim based on sample data.
I - Imputation: The process of filling in missing values in a dataset using statistical methods.
J - Joint Probability: The probability of two or more events occurring together.
K - K-Means Clustering: A popular clustering algorithm that partitions data into K clusters based on similarity.
L - Linear Regression: A statistical method used to model the relationship between a dependent variable and one or more independent variables.
M - Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
N - Normal Distribution: A symmetrical bell-shaped distribution that is commonly used in statistical analysis.
O - Outlier Detection: The process of identifying and removing data points that are significantly different from the rest of the dataset.
P - Precision and Recall: Evaluation metrics used to assess the performance of classification models.
Q - Quantitative Analysis: The process of analyzing numerical data to draw conclusions and make decisions.
R - Random Forest: An ensemble learning algorithm that builds multiple decision trees to improve prediction accuracy.
S - Support Vector Machine (SVM): A supervised learning algorithm used for classification and regression tasks.
T - Time Series Analysis: A statistical technique used to analyze and forecast time-dependent data.
U - Unsupervised Learning: A type of machine learning where the model learns patterns and relationships in data without labeled outputs.
V - Validation Set: A subset of data used to evaluate the performance of a model during training.
W - Web Scraping: The process of extracting data from websites for analysis and visualization.
X - XGBoost: An optimized gradient boosting algorithm that is widely used in machine learning competitions.
Y - Yield Curve Analysis: The study of the relationship between interest rates and the maturity of fixed-income securities.
Z - Z-Score: A standardized score that represents the number of standard deviations a data point is from the mean.
Credits: https://t.me/free4unow_backup
Like if you need similar content 😄👍 | 1 680 |
| 4 |  Target Variables | 3 050 |
| 5 |  Features 💯 | 2 847 |
| 6 | When a dataset contains many features, dimensionality reduction can represent the data using fewer dimensions while retaining important information.
A common technique is:
PCA — Principal Component Analysis
Benefits can include:
• Simplifying data
• Visualization
• Reducing noise
• Improving computational efficiency
39. Model Selection
Different algorithms work better for different problems.
For example:
Linear Regression → Simple numerical prediction
Random Forest → Complex tabular relationships
K-Means → Customer segmentation
Neural Networks → Complex patterns and large-scale data
The best model isn't always the most complicated one.
40. Model Deployment
A trained model is useful only when it can be used in a real application.
Double Tap ❤️ For More
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0.959408 ₽ · /balance_help | 4 799 |
| 7 | Cross-validation provides a more reliable estimate of model performance.
In K-Fold Cross-Validation, the data is divided into K parts.
The model trains and validates multiple times using different portions of the data.
This helps determine whether the model performs consistently.
27. Confusion Matrix
A confusion matrix evaluates classification predictions.
It contains:
• True Positive (TP) → Correct positive prediction
• True Negative (TN) → Correct negative prediction
• False Positive (FP) → Incorrect positive prediction
• False Negative (FN) → Incorrect negative prediction
It forms the foundation for several classification metrics.
28. Accuracy
Accuracy measures the proportion of predictions that are correct.
Accuracy = Correct Predictions / Total Predictions
It can be useful when classes are reasonably balanced.
But accuracy can be misleading for highly imbalanced datasets.
29. Precision
Precision answers:
"Of everything the model predicted as positive, how much was actually positive?"
Important when false positives are costly.
Example:
Fraud detection where investigating every false alarm is expensive.
30. Recall
Recall answers:
"Of all the actual positive cases, how many did the model successfully identify?"
Important when missing a positive case is costly.
Example:
Detecting fraudulent transactions or serious diseases.
31. F1 Score
F1 Score combines Precision and Recall into a single metric.
It is especially useful when you need a balance between precision and recall.
A high F1 score generally means the model performs well on both.
32. ROC-AUC
ROC-AUC measures how well a classification model separates positive and negative classes across different classification thresholds.
A higher AUC generally indicates better discrimination.
It is useful when comparing classification models, especially when the decision threshold can change.
33. Regularization
Regularization helps prevent overfitting by penalizing overly complex models.
Common techniques:
L1 Regularization → Lasso
L2 Regularization → Ridge
The goal is not simply to make the model smaller, but to improve its ability to generalize to unseen data.
34. Decision Trees
A Decision Tree makes predictions by repeatedly splitting data based on feature values.
Example:
Income > ₹10L?
Yes → Higher-risk segment
No → Lower-risk segment
Trees are easy to understand and can handle both classification and regression tasks.
35. Random Forest
Random Forest combines many decision trees.
Each tree makes a prediction and the ensemble combines their results.
Advantages:
• Powerful
• Handles nonlinear relationships
• Less prone to overfitting than a single deep tree
• Works well with many tabular datasets
36. Boosting
Boosting builds models sequentially, with each new model focusing on mistakes made by previous models.
Popular algorithms include:
• XGBoost
• LightGBM
• CatBoost
• AdaBoost
Boosting is widely used for structured/tabular data.
37. Clustering
Clustering groups similar data points without predefined labels.
Example:
A business might group customers into:
• Budget customers
• Regular customers
• High-value customers
K-Means is one of the most commonly used clustering algorithms.
38. Dimensionality Reduction | 3 188 |
| 8 | • Mean Squared Error
• Mean Absolute Error
• Log Loss
• Cross-Entropy Loss
16. Cost Function
A cost function generally represents the overall error across a set of training examples.
For example, in linear regression, Mean Squared Error can be used to measure the average prediction error.
The model attempts to minimize the cost.
17. Gradient Descent
Gradient Descent is an optimization algorithm used to minimize the model's error.
Think of it like standing on a mountain and trying to reach the lowest point.
The algorithm repeatedly adjusts model parameters in a direction that reduces the loss.
18. Learning Rate
The learning rate controls how large each update is during optimization.
Too large:
❌ The model may overshoot the optimal solution.
Too small:
❌ Training may become very slow.
A suitable learning rate helps the model converge efficiently.
19. Parameters vs Hyperparameters
Parameters
Values learned automatically by the model during training.
Examples:
• Regression coefficients
• Neural-network weights
Hyperparameters
Settings chosen before or during model training.
Examples:
• Learning rate
• Number of trees
• Maximum tree depth
• Number of clusters
20. Feature Engineering
Feature engineering means creating or transforming features to help a model learn better.
Example:
Instead of using:
Purchase Date
Create:
Customer Tenure
Other examples:
• Extracting month from a date
• Creating total purchase amount
• Creating customer tenure
• Combining related variables
Good features can significantly improve model performance.
21. Feature Scaling
Features may have very different numerical ranges.
Example:
Age → 18–70
Salary → 20,000–500,000
Scaling transforms features into comparable ranges.
Common techniques:
• Standardization
• Min-Max Scaling
Scaling is particularly important for algorithms such as KNN, SVM, and many optimization-based models.
22. Encoding Categorical Variables
Machine learning algorithms generally need numerical representations of categorical information.
Example:
Gender:
Male
Female
Could be represented using numerical encoding.
Common techniques:
• One-Hot Encoding
• Ordinal Encoding
• Target Encoding
The appropriate method depends on the data and algorithm.
23. Missing Values
Real-world datasets often contain missing information.
Example:
Age:
25
32
Missing
41
Possible approaches:
• Remove rows
• Fill with mean
• Fill with median
• Fill with mode
• Use model-based imputation
The correct approach depends on why the values are missing and the characteristics of the dataset.
24. Outliers
An outlier is a data point that is significantly different from the rest.
Example:
Most salaries → ₹4–15 lakh
One salary → ₹500 lakh
Outliers can sometimes represent errors, but they can also contain valuable information.
Don't automatically remove them.
25. Data Leakage
Data leakage occurs when information that should not be available during prediction accidentally enters the training process.
Example:
Trying to predict whether a customer will default, but including a variable that is only recorded after the customer defaults.
This can produce excellent test results that fail in the real world.
26. Cross-Validation | 1 563 |
| 9 | The model learns from these examples and predicts the salary for someone with 4 years of experience.
Two major types:
Regression → Predict a number
Classification → Predict a category
5. Unsupervised Learning
Here, the data does not have a predefined target.
The model tries to discover hidden patterns or groups.
Example:
A company gives customer purchasing data to an algorithm and asks it to identify different customer groups.
Common techniques:
• K-Means Clustering
• Hierarchical Clustering
• PCA
6. Regression
Regression predicts a continuous numerical value.
Examples:
• House price
• Sales
• Revenue
• Temperature
• Customer lifetime value
Common algorithms:
• Linear Regression
• Polynomial Regression
• Ridge Regression
• Lasso Regression
7. Classification
Classification predicts a category or class.
Examples:
• Spam / Not Spam
• Fraud / Not Fraud
• Customer will churn / Won't churn
• Disease / No Disease
Common algorithms:
• Logistic Regression
• Decision Tree
• Random Forest
• SVM
• KNN
• Naive Bayes
8. Training Data
Training data is the data used to teach the model.
The model analyzes the training examples and learns relationships between features and the target.
More importantly, a good model shouldn't simply memorize the training data.
9. Test Data
Test data is unseen data used to determine how well the trained model performs on new examples.
A common approach is:
Training Data → Learn
Test Data → Evaluate
The goal is to perform well on data the model hasn't seen before.
10. Validation Data
Validation data is used during model development to:
• Compare models
• Tune parameters
• Select features
• Choose the best configuration
A common setup is:
Training → Validation → Test
The test set should ideally remain untouched until final evaluation.
11. Overfitting
Overfitting occurs when a model learns the training data too closely, including noise and random patterns.
Result:
Training performance → Very high
New-data performance → Poor
Example:
A student memorizes every question from a practice test but cannot solve new questions.
Common solutions:
• More training data
• Simpler model
• Regularization
• Cross-validation
• Feature selection
12. Underfitting
Underfitting occurs when a model is too simple to capture the important patterns in the data.
Result:
Training performance → Poor
Test performance → Poor
The model hasn't learned enough.
13. Bias and Variance
These describe two important sources of model error.
High Bias → Model is too simple
Usually associated with underfitting.
High Variance → Model is too sensitive to training data
Usually associated with overfitting.
The goal is to find a good balance between them.
14. Bias-Variance Tradeoff
Increasing model complexity can reduce bias but increase variance.
Reducing complexity can reduce variance but increase bias.
A good ML model tries to achieve a useful balance rather than simply maximizing complexity.
15. Loss Function
A loss function measures how wrong the model's prediction is.
Example:
Actual price = ₹50 lakh
Predicted price = ₹45 lakh
The loss function measures the difference between them.
The training process tries to minimize this loss.
Common examples: | 1 456 |
| 10 | 🚀 Machine Learning: Important Concepts You Should Know
Machine Learning can seem overwhelming because there are many algorithms and techniques. The key is to first understand the core concepts behind how ML works.
1. What is Machine Learning?
Machine Learning is a branch of AI where computers learn patterns from data and use those patterns to make predictions or decisions.
Example:
Give a model historical house data containing size, location, and price. The model learns the relationship between these factors and can predict the price of a new house.
2. Features
Features are the input variables used by a machine learning model.
Example:
For house-price prediction:
• House size
• Number of bedrooms
• Location
• Age of house
These are the features.
Think of features as the information the model uses to make a prediction.
3. Target Variable
The target is what the model is trying to predict.
Example:
Features → Size, bedrooms, location
Target → House price
In supervised learning, the target is usually represented as y, while features are represented as X.
4. Supervised Learning
In supervised learning, the model learns from data where the correct answer is already known.
Example:
Experience | Salary
1 year | ₹4L
3 years | ₹7L
5 years | ₹11L | 1 717 |
| 11 |  🗺️ 6 stops to a data career.
Generative AI → Spreadsheets → SQL → AI assistants & agents → Python → Machine Learning. Then a capstone on your own dataset.
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| 12 | ✅ SQL for Data Science 🗄️📊
👉 SQL is one of the most important skills for Data Scientists and Data Analysts.
Almost every company stores data inside databases, and SQL helps retrieve and analyze that data.
🔹 1. What is SQL?
SQL = Structured Query Language
👉 Used to:
✔ Store data
✔ Retrieve data
✔ Filter data
✔ Analyze data
🔥 2. Common Database Systems
✔ MySQL
✔ PostgreSQL
✔ SQLite
✔ Microsoft SQL Server
🔹 3. Basic SQL Query
✅ SELECT Statement
Used to retrieve data from a table.
SELECT * FROM employees;
👉 ** means all columns.
🔹 4. Select Specific Columns
SELECT name, salary FROM employees;
🔹 5. WHERE Clause ⭐
Used for filtering data.
SELECT * FROM employees
WHERE salary > 50000;
🔹 6. ORDER BY
Sort data.
SELECT * FROM employees
ORDER BY salary DESC;
✔ ASC → Ascending
✔ DESC → Descending
🔹 7. Aggregate Functions ⭐
Used for calculations.
Function: COUNT()
Purpose: Count rows
Function: SUM()
Purpose: Total
Function: AVG()
Purpose: Average
Function: MAX()
Purpose: Highest value
Function: MIN()
Purpose: Lowest value
✅ Example
SELECT AVG(salary)
FROM employees;
🔹 8. GROUP BY ⭐
Used to group data.
SELECT department, AVG(salary)
FROM employees
GROUP BY department;
🔹 9. Why SQL is Important?
✔ Most asked interview skill
✔ Used daily by analysts & data scientists
✔ Essential for working with databases
🎯 Today’s Goal
✔ Learn SELECT queries
✔ Filter using WHERE
✔ Use aggregate functions
✔ Understand GROUP BY
👉 SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v 🗄️🔥
💬 Tap ❤️ for more! | 3 119 |
| 13 |  ⚡️ Kandinsky 6.0 Video vs Competitors: Benchmark Analysis
The flagship of the new open-source Kandinsky 6.0 Video lineup — the Pro model with 29 billion parameters — shows significant improvements over its predecessor and competitors.
📊 Benchmark results:
vs Kandinsky 5.0:
• Clear advantage across all criteria
• Improved visual quality
• Better prompt adherence
• Enhanced camera movement control
vs LTX 2.5 (open-source):
• Preferred in visual quality and speech quality
vs Veo 3.1 Fast (closed-source):
• Superior in image animation tasks
• Better visual quality
• Improved source image adherence
• More natural camera movement
🏗 Technical architecture:
• Dual-stream architecture (video + audio)
• Audio pretrained on 40M tracks; joint training on 7M audio-video segments
• 44 kHz audio output
• Full HD resolution support
• Lip-sync technology
• Lite version (3B) runs on consumer GPUs (16–32 GB VRAM)
🔧 Deployment:
Open-source under MIT license. Available through:
• Diffusers
• FastVideo
• ComfyUI
• SGLang
• vLLM-omni
Anton Frolov, Sber: "We're releasing it free of charge and without restrictions."
🔗 Hugging Face | 3 733 |
| 14 | Machine Learning Roadmap
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus (Gradients, Optimization)
| | |-- Probability and Statistics
| | |-- Matrix Operations
| |
| |-- Programming
| | |-- Python (NumPy, Pandas, Scikit-learn)
| | |-- R (Optional for Statistical Modeling)
| | |-- SQL (For Data Extraction)
|
|-- Data Preprocessing
| |-- Data Cleaning
| |-- Feature Engineering
| | |-- Encoding Categorical Data
| | |-- Feature Scaling (Standardization, Normalization)
| | |-- Handling Missing Values
| |-- Dimensionality Reduction (PCA, LDA)
|
|-- Supervised Learning
| |-- Regression
| | |-- Linear Regression
| | |-- Polynomial Regression
| | |-- Ridge and Lasso Regression
| |-- Classification
| | |-- Logistic Regression
| | |-- Decision Trees
| | |-- Support Vector Machines (SVM)
| | |-- Ensemble Methods (Random Forest, Gradient Boosting, XGBoost)
|
|-- Unsupervised Learning
| |-- Clustering
| | |-- K-Means
| | |-- Hierarchical Clustering
| | |-- DBSCAN
| |-- Dimensionality Reduction
| | |-- Principal Component Analysis (PCA)
| | |-- t-SNE
| |-- Association Rules (Apriori, FP-Growth)
|
|-- Reinforcement Learning
| |-- Markov Decision Processes
| |-- Q-Learning
| |-- Deep Q-Learning
| |-- Policy Gradient Methods
|
|-- Model Evaluation and Optimization
| |-- Train-Test Split and Cross-Validation
| |-- Performance Metrics
| | |-- Accuracy, Precision, Recall, F1-Score
| | |-- ROC-AUC
| | |-- Mean Squared Error (MSE), R-squared
| |-- Hyperparameter Tuning
| | |-- Grid Search
| | |-- Random Search
| | |-- Bayesian Optimization
|
|-- Deep Learning
| |-- Neural Networks
| | |-- Perceptrons
| | |-- Backpropagation
| |-- Convolutional Neural Networks (CNN)
| | |-- Image Classification
| | |-- Object Detection (YOLO, SSD)
| |-- Recurrent Neural Networks (RNN)
| | |-- LSTM
| | |-- GRU
| |-- Transformers (Attention Mechanisms, BERT, GPT)
| |-- Tools and Frameworks (TensorFlow, PyTorch)
|
|-- Advanced Topics
| |-- Transfer Learning
| |-- Generative Adversarial Networks (GANs)
| |-- Reinforcement Learning with Neural Networks
| |-- Explainable AI (SHAP, LIME)
|
|-- Applications of Machine Learning
| |-- Recommender Systems (Collaborative Filtering, Content-Based)
| |-- Fraud Detection
| |-- Sentiment Analysis
| |-- Predictive Maintenance
| |-- Autonomous Vehicles
|
|-- Deployment of Models
| |-- Flask, FastAPI
| |-- Cloud Deployment (AWS SageMaker, Azure ML)
| |-- Containerization (Docker, Kubernetes)
| |-- Model Monitoring and Retraining
Best Resources to learn Machine Learning 👇👇
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING👍👍 | 5 354 |
| 15 |  🚀 𝐁𝐞𝐜𝐨𝐦𝐞 𝐚𝐧 𝐀𝐈 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐢𝐧 𝟐𝟎𝟐𝟔
🎯 Choose Your Learning Track:
💻 Java Full Stack + AI Engineering
🌐 MERN Full Stack + AI Engineering
Placement Highlights: ₹41 LPA highest package | ₹7.4 LPA average package | 2,000+ students placed | 500+ hiring partners
🔗 𝗕𝗼𝗼𝗸 𝗙𝗥𝗘𝗘 𝗗𝗲𝗺𝗼 𝗖𝗹𝗮𝘀𝘀 :- https://pdlink.in/4fWJVID
⚡ AI is creating new career opportunities—start building the skills companies need in 2026! | 4 812 |
| 16 |  Machine Learning Roadmap | 4 597 |
| 17 |  SQL & Python Cheatsheet for Beginners ❤️ | 4 056 |
| 18 | ![#Ad #AI_Models 🔥 GigaChat 3.5 Reasoning [Open-Source] ℹ️ Overview: New LLM that thinks before it answers. Breaks problems in](https://cdn.tlmtr.io/_next/image?url=https%3A%2F%2Fn1.tlmtr.cc%2Fv%2F_u74IP8VHJ7vIO8AuGlc4bjQucvvMGYIZhPDTZs5S5So%3Fty%3Dx&w=64&q=75){kind=small} #Ad
#AI_Models
🔥 GigaChat 3.5 Reasoning [Open-Source]
ℹ️ Overview:
New LLM that thinks before it answers. Breaks problems into stages, builds plans, checks results, and self-corrects using automated verification.
🔗 Source:
Hugging Face fp8 | bf16
📝 Model Specs:
✪ Built on GigaChat 3.5 Ultra with multiple step-by-step reasoning paths
✪ Proprietary linear attention for efficient long contexts
✪ Token-efficient: 37% fewer tokens than DeepSeek V4 Flash Preview
✪ Benchmarks: IFBench 44→77, Natural Plan 64→80, LiveCodeBench v6 56→85
✪ MIT License | 3 781 |
| 19 | ✅ Programming Languages, Libraries & Tools Every Tech Field Uses 👨💻🚀
🧠 DATA SCIENCE & MACHINE LEARNING
1. Python → Pandas, NumPy, TensorFlow, PyTorch
2. R → ggplot2, dplyr, caret
3. SQL → PostgreSQL, MySQL
4. Julia → Flux, Pluto
🤖 ARTIFICIAL INTELLIGENCE
1. Python → Keras, OpenCV, LangChain
2. C++ → OpenCV, CUDA
3. Java → Deeplearning4j
🌐 WEB DEVELOPMENT
1. JavaScript → React, Node.js, Express.js
2. TypeScript → Next.js, Angular
3. PHP → Laravel
4. Python → Django, Flask
📱 APP DEVELOPMENT
1. Kotlin → Android SDK, Jetpack Compose
2. Swift → SwiftUI, UIKit
3. Dart → Flutter
4. JavaScript → React Native
🎮 GAME DEVELOPMENT
1. C++ → Unreal Engine
2. C# → Unity
3. Lua → Roblox Studio
4. Python → Pygame
🔐 CYBER SECURITY
1. Python → Scapy, Requests
2. Bash → Linux Tools
3. PowerShell → Windows Automation
4. Go → Networking Tools
☁️ CLOUD & DEVOPS
1. Go → Docker, Kubernetes
2. Python → Ansible, Boto3
3. Shell Script → Linux Automation
4. YAML → CI/CD Pipelines
💬 Tap ❤️ if this helped you! | 3 111 |
| 20 |  𝗙𝗥𝗘𝗘 𝗔𝗜 𝗖𝗮𝗿𝗲𝗲𝗿 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 🚀
Join this expert-led masterclass and discover how to become industry-ready for high-growth AI roles.
📅 Date: 24 September 2026
⏰ Time: 7:00 PM–9:00 PM IST
🌐 Mode: Online
🎓 Certificate: Available to all attendees
Eligibility :- Graduates Passing In 2025 or earlier
🔗 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 👇
https://pdlink.in/4xAMeGW
⚡ Register now and take your first step towards a successful career in AI! | 2 863 |
