Stanford’s Machine Learning - by Andrew Ng
A complete lecture notes of 227 pages. Available Free.
Download the notes:
cs229.stanford.edu/main_notes.pdf
A complete lecture notes of 227 pages. Available Free.
Download the notes:
cs229.stanford.edu/main_notes.pdf
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras ✅
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❤6
🔤 A–Z of Artificial Intelligence 🤖
A – Algorithm
A step-by-step procedure used by machines to solve problems or perform tasks.
B – Backpropagation
A core technique in training neural networks by minimizing error through gradient descent.
C – Computer Vision
AI field focused on enabling machines to interpret and understand visual information.
D – Deep Learning
A subset of ML using neural networks with many layers to model complex patterns.
E – Ethics in AI
Concerns around fairness, bias, transparency, and responsible AI development.
F – Feature Engineering
The process of selecting and transforming variables to improve model performance.
G – GANs (Generative Adversarial Networks)
Two neural networks competing to generate realistic data, like images or audio.
H – Hyperparameters
Settings like learning rate or batch size that control model training behavior.
I – Inference
Using a trained model to make predictions on new, unseen data.
J – Jupyter Notebook
An interactive coding environment widely used for prototyping and sharing AI projects.
K – K-Means Clustering
A popular unsupervised learning algorithm for grouping similar data points.
L – LSTM (Long Short-Term Memory)
A type of RNN designed to handle long-term dependencies in sequence data.
M – Machine Learning
A core AI technique where systems learn patterns from data to make decisions.
N – NLP (Natural Language Processing)
AI's ability to understand, interpret, and generate human language.
O – Overfitting
When a model learns noise in training data and performs poorly on new data.
P – PyTorch
A flexible deep learning framework popular in research and production.
Q – Q-Learning
A reinforcement learning algorithm that helps agents learn optimal actions.
R – Reinforcement Learning
Training agents to make decisions by rewarding desired behaviors.
S – Supervised Learning
ML where models learn from labeled data to predict outcomes.
T – Transformers
A deep learning architecture powering models like BERT and GPT.
U – Unsupervised Learning
ML where models find patterns in data without labeled outcomes.
V – Validation Set
A subset of data used to tune model parameters and prevent overfitting.
W – Weights
Parameters in neural networks that are adjusted during training to minimize error.
X – XGBoost
A powerful gradient boosting algorithm used for structured data problems.
Y – YOLO (You Only Look Once)
A real-time object detection system used in computer vision.
Z – Zero-shot Learning
AI's ability to make predictions on tasks it hasn’t explicitly been trained on.
Double Tap ♥️ For More
A – Algorithm
A step-by-step procedure used by machines to solve problems or perform tasks.
B – Backpropagation
A core technique in training neural networks by minimizing error through gradient descent.
C – Computer Vision
AI field focused on enabling machines to interpret and understand visual information.
D – Deep Learning
A subset of ML using neural networks with many layers to model complex patterns.
E – Ethics in AI
Concerns around fairness, bias, transparency, and responsible AI development.
F – Feature Engineering
The process of selecting and transforming variables to improve model performance.
G – GANs (Generative Adversarial Networks)
Two neural networks competing to generate realistic data, like images or audio.
H – Hyperparameters
Settings like learning rate or batch size that control model training behavior.
I – Inference
Using a trained model to make predictions on new, unseen data.
J – Jupyter Notebook
An interactive coding environment widely used for prototyping and sharing AI projects.
K – K-Means Clustering
A popular unsupervised learning algorithm for grouping similar data points.
L – LSTM (Long Short-Term Memory)
A type of RNN designed to handle long-term dependencies in sequence data.
M – Machine Learning
A core AI technique where systems learn patterns from data to make decisions.
N – NLP (Natural Language Processing)
AI's ability to understand, interpret, and generate human language.
O – Overfitting
When a model learns noise in training data and performs poorly on new data.
P – PyTorch
A flexible deep learning framework popular in research and production.
Q – Q-Learning
A reinforcement learning algorithm that helps agents learn optimal actions.
R – Reinforcement Learning
Training agents to make decisions by rewarding desired behaviors.
S – Supervised Learning
ML where models learn from labeled data to predict outcomes.
T – Transformers
A deep learning architecture powering models like BERT and GPT.
U – Unsupervised Learning
ML where models find patterns in data without labeled outcomes.
V – Validation Set
A subset of data used to tune model parameters and prevent overfitting.
W – Weights
Parameters in neural networks that are adjusted during training to minimize error.
X – XGBoost
A powerful gradient boosting algorithm used for structured data problems.
Y – YOLO (You Only Look Once)
A real-time object detection system used in computer vision.
Z – Zero-shot Learning
AI's ability to make predictions on tasks it hasn’t explicitly been trained on.
Double Tap ♥️ For More
❤12
👨💻 FREE Resources to Practice Python with Projects
1. http://www.pythonchallenge.com/
2. https://www.hackerrank.com/domains/python
3. https://www.hackerearth.com/practice/python/getting-started/numbers/practice-problems/
4. https://learnpython.org/
5. https://www.w3schools.com/python/python_exercises.asp
6. http://www.pythonchallenge.com/
7. http://codingbat.com/python
8. https://pythonbasics.org/exercises/
9. https://news.1rj.ru/str/pythondevelopersindia/300
10. https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
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4. https://learnpython.org/
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7. http://codingbat.com/python
8. https://pythonbasics.org/exercises/
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🧠 7 Smart Tips to Crack Machine Learning Interviews 🚀📈
1️⃣ Understand ML End-to-End
⦁ Know the pipeline: data prep → modeling → evaluation → deployment
⦁ Be clear on supervised vs unsupervised learning
2️⃣ Focus on Feature Engineering
⦁ Show how you create useful features
⦁ Explain how they impact model performance
3️⃣ Communicate Clearly
⦁ Simplify complex topics
⦁ Use structured answers: Problem → Approach → Result
4️⃣ Be Ready for Code Questions
⦁ Practice with NumPy, Pandas, and Scikit-learn
⦁ Be comfortable writing clean, testable functions
5️⃣ Model Selection Logic
⦁ Don’t just say you used XGBoost
⦁ Explain why it fits your problem
6️⃣ Tackle ML Ops Questions
⦁ Learn basics of deployment, APIs, model monitoring
⦁ Understand tools like Docker, MLflow
7️⃣ Practice Mock Interviews
⦁ Simulate pressure
⦁ Get feedback on technical + communication skills
💬 Double tap ❤️ for more!
1️⃣ Understand ML End-to-End
⦁ Know the pipeline: data prep → modeling → evaluation → deployment
⦁ Be clear on supervised vs unsupervised learning
2️⃣ Focus on Feature Engineering
⦁ Show how you create useful features
⦁ Explain how they impact model performance
3️⃣ Communicate Clearly
⦁ Simplify complex topics
⦁ Use structured answers: Problem → Approach → Result
4️⃣ Be Ready for Code Questions
⦁ Practice with NumPy, Pandas, and Scikit-learn
⦁ Be comfortable writing clean, testable functions
5️⃣ Model Selection Logic
⦁ Don’t just say you used XGBoost
⦁ Explain why it fits your problem
6️⃣ Tackle ML Ops Questions
⦁ Learn basics of deployment, APIs, model monitoring
⦁ Understand tools like Docker, MLflow
7️⃣ Practice Mock Interviews
⦁ Simulate pressure
⦁ Get feedback on technical + communication skills
💬 Double tap ❤️ for more!
❤2👍1
✅ Top Machine Learning Projects That Strengthen Your Resume 🧠💼
1. House Price Prediction
→ Use regression with Scikit-learn on Boston or Kaggle datasets
→ Feature engineering and evaluation with RMSE for real estate insights
2. Iris Flower Classification
→ Apply logistic regression or decision trees on classic UCI data
→ Visualize clusters and accuracy metrics like confusion matrices
3. Titanic Survival Prediction
→ Handle missing data and build classifiers with Random Forests
→ Interpret feature importance for demographic survival factors
4. Credit Card Fraud Detection
→ Tackle imbalanced data using SMOTE and isolation forests
→ Deploy anomaly detection with precision-recall for financial security
5. Movie Recommendation System
→ Implement collaborative filtering with Surprise or matrix factorization
→ Evaluate with NDCG and personalize suggestions based on user ratings
6. Handwritten Digit Recognition
→ Train CNNs with TensorFlow on MNIST dataset
→ Achieve high accuracy and add real-time prediction for digit input
7. Customer Churn Prediction
→ Model telecom data with XGBoost for retention forecasts
→ Include SHAP explanations and business impact simulations
Tips:
⦁ Leverage libraries like Scikit-learn, TensorFlow, and PyTorch for scalability
⦁ Deploy via Streamlit or Flask and track with MLflow for production readiness
⦁ Focus on metrics, ethics, and GitHub repos with detailed READMEs
💬 Tap ❤️ for more!
1. House Price Prediction
→ Use regression with Scikit-learn on Boston or Kaggle datasets
→ Feature engineering and evaluation with RMSE for real estate insights
2. Iris Flower Classification
→ Apply logistic regression or decision trees on classic UCI data
→ Visualize clusters and accuracy metrics like confusion matrices
3. Titanic Survival Prediction
→ Handle missing data and build classifiers with Random Forests
→ Interpret feature importance for demographic survival factors
4. Credit Card Fraud Detection
→ Tackle imbalanced data using SMOTE and isolation forests
→ Deploy anomaly detection with precision-recall for financial security
5. Movie Recommendation System
→ Implement collaborative filtering with Surprise or matrix factorization
→ Evaluate with NDCG and personalize suggestions based on user ratings
6. Handwritten Digit Recognition
→ Train CNNs with TensorFlow on MNIST dataset
→ Achieve high accuracy and add real-time prediction for digit input
7. Customer Churn Prediction
→ Model telecom data with XGBoost for retention forecasts
→ Include SHAP explanations and business impact simulations
Tips:
⦁ Leverage libraries like Scikit-learn, TensorFlow, and PyTorch for scalability
⦁ Deploy via Streamlit or Flask and track with MLflow for production readiness
⦁ Focus on metrics, ethics, and GitHub repos with detailed READMEs
💬 Tap ❤️ for more!
❤11👍3
🤖 CHATGPT CHEAT SHEET
🧠 Master prompting by giving ChatGPT the right role, goal, style & format!
🎭 Give a Role
⦁ Act as a writer
⦁ Act as a software engineer
⦁ Act as a YouTuber
⦁ Act as a proofreader
⦁ Act as a researcher
🎯 Define the Goal
⦁ Write a blog post
⦁ Proofread this email
⦁ Give me a recipe for...
⦁ Analyze this text
⦁ Write a noscript for a video
⚙️ Set Restrictions
⦁ Use simple language
⦁ Be concise
⦁ Write in a persuasive tone
⦁ Use scientific sources
⦁ Write in basic English
📑 Define Format
⦁ Answer in bullet points
⦁ Include subheadings
⦁ Use a numbered list
⦁ Add emojis
⦁ Answer using code
✅ Example Prompt:
"Act as a professional copywriter. Write a blog post on 'How to Stay Focused While Studying'. Use simple English, write in a friendly tone, and format it with subheadings and bullet points."
💡 Double Tap ♥️ For More
🧠 Master prompting by giving ChatGPT the right role, goal, style & format!
🎭 Give a Role
⦁ Act as a writer
⦁ Act as a software engineer
⦁ Act as a YouTuber
⦁ Act as a proofreader
⦁ Act as a researcher
🎯 Define the Goal
⦁ Write a blog post
⦁ Proofread this email
⦁ Give me a recipe for...
⦁ Analyze this text
⦁ Write a noscript for a video
⚙️ Set Restrictions
⦁ Use simple language
⦁ Be concise
⦁ Write in a persuasive tone
⦁ Use scientific sources
⦁ Write in basic English
📑 Define Format
⦁ Answer in bullet points
⦁ Include subheadings
⦁ Use a numbered list
⦁ Add emojis
⦁ Answer using code
✅ Example Prompt:
"Act as a professional copywriter. Write a blog post on 'How to Stay Focused While Studying'. Use simple English, write in a friendly tone, and format it with subheadings and bullet points."
💡 Double Tap ♥️ For More
❤8👍2
🌐 Machine Learning Tools & Their Use Cases 🧠🔄
🔹 TensorFlow ➜ Building scalable deep learning models for production deployment
🔹 PyTorch ➜ Flexible research and dynamic neural networks for rapid prototyping
🔹 Scikit-learn ➜ Traditional ML algorithms like classification and clustering on structured data
🔹 Keras ➜ High-level API for quick neural network building and experimentation
🔹 XGBoost ➜ Gradient boosting for high-accuracy predictions on tabular data
🔹 Hugging Face Transformers ➜ Pre-trained NLP models for text generation and sentiment analysis
🔹 LightGBM ➜ Fast gradient boosting with efficient handling of large datasets
🔹 OpenCV ➜ Computer vision tasks like image processing and object detection
🔹 MLflow ➜ Experiment tracking, model versioning, and lifecycle management
🔹 Jupyter Notebook ➜ Interactive coding, visualization, and sharing ML workflows
🔹 Apache Spark MLlib ➜ Distributed big data processing for scalable ML pipelines
🔹 Git ➜ Version control for collaborative ML project development
🔹 Docker ➜ Containerizing ML models for consistent deployment environments
🔹 AWS SageMaker ➜ Cloud-based training, tuning, and hosting of ML models
🔹 Pandas ➜ Data manipulation and preprocessing for ML datasets
💬 Tap ❤️ if this helped!
🔹 TensorFlow ➜ Building scalable deep learning models for production deployment
🔹 PyTorch ➜ Flexible research and dynamic neural networks for rapid prototyping
🔹 Scikit-learn ➜ Traditional ML algorithms like classification and clustering on structured data
🔹 Keras ➜ High-level API for quick neural network building and experimentation
🔹 XGBoost ➜ Gradient boosting for high-accuracy predictions on tabular data
🔹 Hugging Face Transformers ➜ Pre-trained NLP models for text generation and sentiment analysis
🔹 LightGBM ➜ Fast gradient boosting with efficient handling of large datasets
🔹 OpenCV ➜ Computer vision tasks like image processing and object detection
🔹 MLflow ➜ Experiment tracking, model versioning, and lifecycle management
🔹 Jupyter Notebook ➜ Interactive coding, visualization, and sharing ML workflows
🔹 Apache Spark MLlib ➜ Distributed big data processing for scalable ML pipelines
🔹 Git ➜ Version control for collaborative ML project development
🔹 Docker ➜ Containerizing ML models for consistent deployment environments
🔹 AWS SageMaker ➜ Cloud-based training, tuning, and hosting of ML models
🔹 Pandas ➜ Data manipulation and preprocessing for ML datasets
💬 Tap ❤️ if this helped!
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Coding Roadmaps
• Frontend : https://roadmap.sh/frontend
• Backend : https://roadmap.sh/backend
• Devops : https://roadmap.sh/devops
• Reactjs : https://roadmap.sh/react
• Android : https://roadmap.sh/android
• Angular : https://roadmap.sh/angular
• Python : https://roadmap.sh/python
• Golang : https://roadmap.sh/golang
• Java : https://roadmap.sh/java
Useful Cheatsheets
Data Science
SQL
Java Programming
PHP
Ruby
Pandas in 5 minutes
Python
GIT and Machine Learning
Javanoscript
HTML
Supervised Learning
Cybersecurity
Reinforcement Learning
VS Code
Join @free4unow_backup for more free resourses
ENJOY LEARNING 👍👍
• Frontend : https://roadmap.sh/frontend
• Backend : https://roadmap.sh/backend
• Devops : https://roadmap.sh/devops
• Reactjs : https://roadmap.sh/react
• Android : https://roadmap.sh/android
• Angular : https://roadmap.sh/angular
• Python : https://roadmap.sh/python
• Golang : https://roadmap.sh/golang
• Java : https://roadmap.sh/java
Useful Cheatsheets
Data Science
SQL
Java Programming
PHP
Ruby
Pandas in 5 minutes
Python
GIT and Machine Learning
Javanoscript
HTML
Supervised Learning
Cybersecurity
Reinforcement Learning
VS Code
Join @free4unow_backup for more free resourses
ENJOY LEARNING 👍👍
❤6
✅ Machine Learning Explained for Beginners 🤖📚
📌 Definition:
Machine Learning (ML) is a type of artificial intelligence that allows systems to learn from data and make decisions or predictions without being explicitly programmed for every task.
1️⃣ How It Works:
ML systems are trained on historical data to identify patterns. Once trained, they apply those patterns to new, unseen data.
Example: Feed a model emails labeled "spam" or "not spam," and it learns how to filter spam automatically.
2️⃣ Types of Machine Learning:
a) Supervised Learning
⦁ Learns from labeled data (inputs + expected outputs)
⦁ Examples: Email classification, price prediction
b) Unsupervised Learning
⦁ Learns from unlabeled data
⦁ Examples: Customer segmentation, topic modeling
c) Reinforcement Learning
⦁ Learns by interacting with the environment and receiving rewards
⦁ Examples: Game AI, robotics
3️⃣ Common Use Cases:
⦁ Recommender systems (Netflix, Amazon)
⦁ Face recognition
⦁ Voice assistants (Alexa, Siri)
⦁ Credit card fraud detection
⦁ Predicting customer churn
4️⃣ Why It Matters:
ML powers smart systems and automates complex decisions. It's used across industries for improving speed, accuracy, and personalization.
5️⃣ Key Terms You’ll Hear Often:
⦁ Model: The trained algorithm
⦁ Dataset: Data used to train or test
⦁ Features: Input variables
⦁ Labels: Target outputs
⦁ Training: Feeding data to the model
⦁ Prediction: The model's output
💡 Start with simple projects like spam detection or house price prediction using Python and scikit-learn.
💬 Tap ❤️ for more!
📌 Definition:
Machine Learning (ML) is a type of artificial intelligence that allows systems to learn from data and make decisions or predictions without being explicitly programmed for every task.
1️⃣ How It Works:
ML systems are trained on historical data to identify patterns. Once trained, they apply those patterns to new, unseen data.
Example: Feed a model emails labeled "spam" or "not spam," and it learns how to filter spam automatically.
2️⃣ Types of Machine Learning:
a) Supervised Learning
⦁ Learns from labeled data (inputs + expected outputs)
⦁ Examples: Email classification, price prediction
b) Unsupervised Learning
⦁ Learns from unlabeled data
⦁ Examples: Customer segmentation, topic modeling
c) Reinforcement Learning
⦁ Learns by interacting with the environment and receiving rewards
⦁ Examples: Game AI, robotics
3️⃣ Common Use Cases:
⦁ Recommender systems (Netflix, Amazon)
⦁ Face recognition
⦁ Voice assistants (Alexa, Siri)
⦁ Credit card fraud detection
⦁ Predicting customer churn
4️⃣ Why It Matters:
ML powers smart systems and automates complex decisions. It's used across industries for improving speed, accuracy, and personalization.
5️⃣ Key Terms You’ll Hear Often:
⦁ Model: The trained algorithm
⦁ Dataset: Data used to train or test
⦁ Features: Input variables
⦁ Labels: Target outputs
⦁ Training: Feeding data to the model
⦁ Prediction: The model's output
💡 Start with simple projects like spam detection or house price prediction using Python and scikit-learn.
💬 Tap ❤️ for more!
❤11👍2👎2
Sber presented Europe’s largest open-source project at AI Journey as it opened access to its flagship models — the GigaChat Ultra-Preview and Lightning, in addition to a new generation of the GigaAM-v3 open-source models for speech recognition and a full range of image and video generation models in the new Kandinsky 5.0 line, including the Video Pro, Video Lite and Image Lite.
The GigaChat Ultra-Preview, a new MoE model featuring 702 billion parameters, has been compiled specifically with the Russian language in mind and trained entirely from scratch. Read a detailed post from the team here.
For the first time in Russia, an MoE model of this scale has been trained entirely from scratch — without relying on any foreign weights. Training from scratch, and on such a scale to boot, is a challenge that few teams in the world have taken on.
Our flagship Kandinsky Video Pro model has caught up with Veo 3 in terms of visual quality and surpassed Wan 2.2-A14B. Read a detailed post from the team here.
The code and weights for all models are now available to all users under MIT license, including commercial use.
The GigaChat Ultra-Preview, a new MoE model featuring 702 billion parameters, has been compiled specifically with the Russian language in mind and trained entirely from scratch. Read a detailed post from the team here.
For the first time in Russia, an MoE model of this scale has been trained entirely from scratch — without relying on any foreign weights. Training from scratch, and on such a scale to boot, is a challenge that few teams in the world have taken on.
Our flagship Kandinsky Video Pro model has caught up with Veo 3 in terms of visual quality and surpassed Wan 2.2-A14B. Read a detailed post from the team here.
The code and weights for all models are now available to all users under MIT license, including commercial use.
AI Journey
AI Journey Conference on 19-21 November 2025. Key speakers in the area of artificial intelligence technology
AI Journey Conference on 19-21 November 2025. Key speakers in the area of artificial intelligence technology.
❤5👍2
✅ Roadmap to Become a Data Scientist 🧪📊
1. Strong Foundation
⦁ Advanced Math & Stats: Linear algebra, calculus, probability
⦁ Programming: Python or R (advanced skills)
⦁ Data Wrangling & Cleaning
2. Machine Learning Basics
⦁ Supervised & unsupervised learning
⦁ Regression, classification, clustering
⦁ Libraries: Scikit-learn, TensorFlow, Keras
3. Data Visualization
⦁ Master Matplotlib, Seaborn, Plotly
⦁ Build dashboards with Tableau or Power BI
4. Deep Learning & NLP
⦁ Neural networks, CNN, RNN
⦁ Natural Language Processing basics
5. Big Data Technologies
⦁ Hadoop, Spark, Kafka
⦁ Cloud platforms: AWS, Azure, GCP
6. Model Deployment
⦁ Flask/Django for APIs
⦁ Docker, Kubernetes basics
7. Projects & Portfolio
⦁ Real-world datasets
⦁ Competitions on Kaggle
8. Communication & Storytelling
⦁ Explain complex insights simply
⦁ Visual & written reports
9. Interview Prep
⦁ Data structures, algorithms
⦁ ML concepts, case studies
💬 Tap ❤️ for more!
1. Strong Foundation
⦁ Advanced Math & Stats: Linear algebra, calculus, probability
⦁ Programming: Python or R (advanced skills)
⦁ Data Wrangling & Cleaning
2. Machine Learning Basics
⦁ Supervised & unsupervised learning
⦁ Regression, classification, clustering
⦁ Libraries: Scikit-learn, TensorFlow, Keras
3. Data Visualization
⦁ Master Matplotlib, Seaborn, Plotly
⦁ Build dashboards with Tableau or Power BI
4. Deep Learning & NLP
⦁ Neural networks, CNN, RNN
⦁ Natural Language Processing basics
5. Big Data Technologies
⦁ Hadoop, Spark, Kafka
⦁ Cloud platforms: AWS, Azure, GCP
6. Model Deployment
⦁ Flask/Django for APIs
⦁ Docker, Kubernetes basics
7. Projects & Portfolio
⦁ Real-world datasets
⦁ Competitions on Kaggle
8. Communication & Storytelling
⦁ Explain complex insights simply
⦁ Visual & written reports
9. Interview Prep
⦁ Data structures, algorithms
⦁ ML concepts, case studies
💬 Tap ❤️ for more!
❤5
List of AI Project Ideas 👨🏻💻🤖
Beginner Projects
🔹 Sentiment Analyzer
🔹 Image Classifier
🔹 Spam Detection System
🔹 Face Detection
🔹 Chatbot (Rule-based)
🔹 Movie Recommendation System
🔹 Handwritten Digit Recognition
🔹 Speech-to-Text Converter
🔹 AI-Powered Calculator
🔹 AI Hangman Game
Intermediate Projects
🔸 AI Virtual Assistant
🔸 Fake News Detector
🔸 Music Genre Classification
🔸 AI Resume Screener
🔸 Style Transfer App
🔸 Real-Time Object Detection
🔸 Chatbot with Memory
🔸 Autocorrect Tool
🔸 Face Recognition Attendance System
🔸 AI Sudoku Solver
Advanced Projects
🔺 AI Stock Predictor
🔺 AI Writer (GPT-based)
🔺 AI-powered Resume Builder
🔺 Deepfake Generator
🔺 AI Lawyer Assistant
🔺 AI-Powered Medical Diagnosis
🔺 AI-based Game Bot
🔺 Custom Voice Cloning
🔺 Multi-modal AI App
🔺 AI Research Paper Summarizer
React ❤️ for more
Beginner Projects
🔹 Sentiment Analyzer
🔹 Image Classifier
🔹 Spam Detection System
🔹 Face Detection
🔹 Chatbot (Rule-based)
🔹 Movie Recommendation System
🔹 Handwritten Digit Recognition
🔹 Speech-to-Text Converter
🔹 AI-Powered Calculator
🔹 AI Hangman Game
Intermediate Projects
🔸 AI Virtual Assistant
🔸 Fake News Detector
🔸 Music Genre Classification
🔸 AI Resume Screener
🔸 Style Transfer App
🔸 Real-Time Object Detection
🔸 Chatbot with Memory
🔸 Autocorrect Tool
🔸 Face Recognition Attendance System
🔸 AI Sudoku Solver
Advanced Projects
🔺 AI Stock Predictor
🔺 AI Writer (GPT-based)
🔺 AI-powered Resume Builder
🔺 Deepfake Generator
🔺 AI Lawyer Assistant
🔺 AI-Powered Medical Diagnosis
🔺 AI-based Game Bot
🔺 Custom Voice Cloning
🔺 Multi-modal AI App
🔺 AI Research Paper Summarizer
React ❤️ for more
❤13
SQL Interview Questions! 🔥🚀
Basic SQL Interview Questions:
- What is SQL?
- What are the different types of SQL commands?
- What is the difference between DDL, DML, DCL, and TCL?
- What is the difference between SQL and MySQL?
- What is a primary key?
- What is a foreign key?
- What is a unique key?
- What is the difference between primary key and unique key?
- What is the difference between HAVING and WHERE?
- What are constraints in SQL? Name a few.
- What is the difference between CHAR and VARCHAR?
- What is Normalization? What are its types?
- What is Denormalization?
- What is an index in SQL?
- What are the different types of indexes?
- What is the difference between Clustered and Non-clustered indexes?
- What is an alias in SQL?
- What is the difference between DELETE and TRUNCATE?
- What is the difference between TRUNCATE and DROP?
- What is a view in SQL?
-------------------------------------
Intermediate SQL Interview Questions:
What is a self-join?
What is an inner join?
What is the difference between INNER JOIN and OUTER JOIN?
What are the types of OUTER JOIN?
What is a cross join?
What is a Cartesian join?
What is the difference between UNION and UNION ALL?
What is the difference between JOIN and UNION?
What is a stored procedure?
What is a trigger in SQL?
What are the different types of triggers?
What is the difference between HAVING and GROUP BY?
What are subqueries?
What are correlated subqueries?
What is an EXISTS clause in SQL?
What is the difference between EXISTS and IN?
What is a cursor in SQL?
What is the difference between OLTP and OLAP?
What are ACID properties in SQL?
What is normalization? Explain 1NF, 2NF, 3NF, and BCNF.
What is a composite key?
What is a surrogate key?
What is the use of the COALESCE function?
What is the difference between IS
NULL and IS NOT NULL?
What is partitioning in SQL?
-------------------------------------
Advanced SQL Interview Questions:
What are window functions in SQL?
What is CTE (Common Table Expression)?
What is the difference between TEMP TABLE and CTE?
What is the difference between RANK(), DENSE_RANK(), and ROW_NUMBER()?
What is a materialized view?
What is the difference between materialized views and normal views?
What is sharding in SQL?
What is the MERGE statement?
What is the JSON data type in SQL?
What is recursive CTE?
What is the difference between LEFT JOIN and LEFT OUTER JOIN?
How does indexing impact performance?
What is the difference between OLAP and OLTP?
What is ETL (Extract, Transform, Load)?
What are window functions? Explain LEAD, LAG, and NTILE.
What is a pivot table in SQL?
What is Dynamic SQL?
What is a NoSQL database? How is it different from SQL databases?
What is the difference between SQL and PL/SQL?
How to find the N-th highest salary in SQL?
-------------------------------------
Practical SQL Queries:
Find the second highest salary from an Employee table.
Find duplicate records in a table.
Write a SQL query to find the count of employees in each department.
Write a query to find employees who earn more than their managers.
Write a query to fetch the first three characters of a string.
Write a SQL query to swap two columns in a table without using a temporary table.
Write a query to find all employees who joined in the last 6 months.
Write a query to find the most repeated values in a column.
Write a query to delete duplicate rows from a table.
Write a SQL query to find all customers who made more than 5 purchases.
React ♥️ for more content like this 👍
Here you can find essential SQL Interview Resources👇
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like this post if you need more 👍❤️
Hope it helps :)
Basic SQL Interview Questions:
- What is SQL?
- What are the different types of SQL commands?
- What is the difference between DDL, DML, DCL, and TCL?
- What is the difference between SQL and MySQL?
- What is a primary key?
- What is a foreign key?
- What is a unique key?
- What is the difference between primary key and unique key?
- What is the difference between HAVING and WHERE?
- What are constraints in SQL? Name a few.
- What is the difference between CHAR and VARCHAR?
- What is Normalization? What are its types?
- What is Denormalization?
- What is an index in SQL?
- What are the different types of indexes?
- What is the difference between Clustered and Non-clustered indexes?
- What is an alias in SQL?
- What is the difference between DELETE and TRUNCATE?
- What is the difference between TRUNCATE and DROP?
- What is a view in SQL?
-------------------------------------
Intermediate SQL Interview Questions:
What is a self-join?
What is an inner join?
What is the difference between INNER JOIN and OUTER JOIN?
What are the types of OUTER JOIN?
What is a cross join?
What is a Cartesian join?
What is the difference between UNION and UNION ALL?
What is the difference between JOIN and UNION?
What is a stored procedure?
What is a trigger in SQL?
What are the different types of triggers?
What is the difference between HAVING and GROUP BY?
What are subqueries?
What are correlated subqueries?
What is an EXISTS clause in SQL?
What is the difference between EXISTS and IN?
What is a cursor in SQL?
What is the difference between OLTP and OLAP?
What are ACID properties in SQL?
What is normalization? Explain 1NF, 2NF, 3NF, and BCNF.
What is a composite key?
What is a surrogate key?
What is the use of the COALESCE function?
What is the difference between IS
NULL and IS NOT NULL?
What is partitioning in SQL?
-------------------------------------
Advanced SQL Interview Questions:
What are window functions in SQL?
What is CTE (Common Table Expression)?
What is the difference between TEMP TABLE and CTE?
What is the difference between RANK(), DENSE_RANK(), and ROW_NUMBER()?
What is a materialized view?
What is the difference between materialized views and normal views?
What is sharding in SQL?
What is the MERGE statement?
What is the JSON data type in SQL?
What is recursive CTE?
What is the difference between LEFT JOIN and LEFT OUTER JOIN?
How does indexing impact performance?
What is the difference between OLAP and OLTP?
What is ETL (Extract, Transform, Load)?
What are window functions? Explain LEAD, LAG, and NTILE.
What is a pivot table in SQL?
What is Dynamic SQL?
What is a NoSQL database? How is it different from SQL databases?
What is the difference between SQL and PL/SQL?
How to find the N-th highest salary in SQL?
-------------------------------------
Practical SQL Queries:
Find the second highest salary from an Employee table.
Find duplicate records in a table.
Write a SQL query to find the count of employees in each department.
Write a query to find employees who earn more than their managers.
Write a query to fetch the first three characters of a string.
Write a SQL query to swap two columns in a table without using a temporary table.
Write a query to find all employees who joined in the last 6 months.
Write a query to find the most repeated values in a column.
Write a query to delete duplicate rows from a table.
Write a SQL query to find all customers who made more than 5 purchases.
React ♥️ for more content like this 👍
Here you can find essential SQL Interview Resources👇
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like this post if you need more 👍❤️
Hope it helps :)
❤7👍2
𝐖𝐡𝐚𝐭 𝐆𝐨𝐨𝐠𝐥𝐞 𝐣𝐮𝐬𝐭 𝐮𝐧𝐥𝐨𝐜𝐤𝐞𝐝 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐰𝐨𝐫𝐥𝐝:
A complete beginner-friendly pathway to understand Generative AI, LLMs, prompt design, and responsible AI.
If you’ve been wanting to break into AI or strengthen your fundamentals, start here 👇
𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫-𝐅𝐫𝐢𝐞𝐧𝐝𝐥𝐲 𝐂𝐨𝐮𝐫𝐬𝐞𝐬:
1️⃣ Introduction to Generative AI
https://lnkd.in/gGDuMktB
2️⃣ Introduction to Large Language Models (LLMs)
https://lnkd.in/gKs4M7pa
3️⃣ Introduction to Responsible AI
https://lnkd.in/gShBAaUk
4️⃣ Prompt Design in Vertex AI
https://lnkd.in/gyy56tAs
5️⃣ Responsible AI: Applying AI Principles with Google Cloud
https://lnkd.in/gHxTvXQB
𝐌𝐲 𝐭𝐚𝐤𝐞 𝐚𝐬 𝐚𝐧 𝐀𝐈 𝐥𝐞𝐚𝐝𝐞𝐫:
The AI wave isn’t coming, it’s already here.
What counted as “advanced knowledge” two years ago is basic literacy today.
* If you’re a student, this is a head start.
* If you’re a professional, this is upskilling gold.
* If you’re a leader, this is a blueprint for future-ready teams.
The people who win in AI aren’t the ones who know the most,
they’re the ones who start early.
A complete beginner-friendly pathway to understand Generative AI, LLMs, prompt design, and responsible AI.
If you’ve been wanting to break into AI or strengthen your fundamentals, start here 👇
𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫-𝐅𝐫𝐢𝐞𝐧𝐝𝐥𝐲 𝐂𝐨𝐮𝐫𝐬𝐞𝐬:
1️⃣ Introduction to Generative AI
https://lnkd.in/gGDuMktB
2️⃣ Introduction to Large Language Models (LLMs)
https://lnkd.in/gKs4M7pa
3️⃣ Introduction to Responsible AI
https://lnkd.in/gShBAaUk
4️⃣ Prompt Design in Vertex AI
https://lnkd.in/gyy56tAs
5️⃣ Responsible AI: Applying AI Principles with Google Cloud
https://lnkd.in/gHxTvXQB
𝐌𝐲 𝐭𝐚𝐤𝐞 𝐚𝐬 𝐚𝐧 𝐀𝐈 𝐥𝐞𝐚𝐝𝐞𝐫:
The AI wave isn’t coming, it’s already here.
What counted as “advanced knowledge” two years ago is basic literacy today.
* If you’re a student, this is a head start.
* If you’re a professional, this is upskilling gold.
* If you’re a leader, this is a blueprint for future-ready teams.
The people who win in AI aren’t the ones who know the most,
they’re the ones who start early.
❤4👌1
Normalization vs Standardization: Why They’re Not the Same
People treat these two as interchangeable. they’re not.
👉 Normalization (Min-Max scaling):
Compresses values to 0–1.
Useful when magnitude matters (pixel values, distances).
👉 Standardization (Z-score):
Centers data around mean=0, std=1.
Useful when distribution shape matters (linear/logistic regression, PCA).
🔑 Key idea:
Normalization preserves relative proportions.
Standardization preserves statistical structure.
Pick the wrong one, and your model’s geometry becomes distorted.
People treat these two as interchangeable. they’re not.
👉 Normalization (Min-Max scaling):
Compresses values to 0–1.
Useful when magnitude matters (pixel values, distances).
👉 Standardization (Z-score):
Centers data around mean=0, std=1.
Useful when distribution shape matters (linear/logistic regression, PCA).
🔑 Key idea:
Normalization preserves relative proportions.
Standardization preserves statistical structure.
Pick the wrong one, and your model’s geometry becomes distorted.
❤4👍4