1.What are the conditions for Overfitting and Underfitting?
Ans:
• In Overfitting the model performs well for the training data, but for any new data it fails to provide output. For Underfitting the model is very simple and not able to identify the correct relationship. Following are the bias and variance conditions.
• Overfitting – Low bias and High Variance results in the overfitted model. The decision tree is more prone to Overfitting.
• Underfitting – High bias and Low Variance. Such a model doesn’t perform well on test data also. For example – Linear Regression is more prone to Underfitting.
2. Which models are more prone to Overfitting?
Ans: Complex models, like the Random Forest, Neural Networks, and XGBoost are more prone to overfitting. Simpler models, like linear regression, can overfit too – this typically happens when there are more features than the number of instances in the training data.
3. When does feature scaling should be done?
Ans: We need to perform Feature Scaling when we are dealing with Gradient Descent Based algorithms (Linear and Logistic Regression, Neural Network) and Distance-based algorithms (KNN, K-means, SVM) as these are very sensitive to the range of the data points.
4. What is a logistic function? What is the range of values of a logistic function?
Ans. f(z) = 1/(1+e -z )
The values of a logistic function will range from 0 to 1. The values of Z will vary from -infinity to +infinity.
5. What are the drawbacks of a linear model?
Ans. There are a couple of drawbacks of a linear model:
A linear model holds some strong assumptions that may not be true in application. It assumes a linear relationship, multivariate normality, no or little multicollinearity, no auto-correlation, and homoscedasticity
A linear model can’t be used for discrete or binary outcomes.
You can’t vary the model flexibility of a linear model.
Ans:
• In Overfitting the model performs well for the training data, but for any new data it fails to provide output. For Underfitting the model is very simple and not able to identify the correct relationship. Following are the bias and variance conditions.
• Overfitting – Low bias and High Variance results in the overfitted model. The decision tree is more prone to Overfitting.
• Underfitting – High bias and Low Variance. Such a model doesn’t perform well on test data also. For example – Linear Regression is more prone to Underfitting.
2. Which models are more prone to Overfitting?
Ans: Complex models, like the Random Forest, Neural Networks, and XGBoost are more prone to overfitting. Simpler models, like linear regression, can overfit too – this typically happens when there are more features than the number of instances in the training data.
3. When does feature scaling should be done?
Ans: We need to perform Feature Scaling when we are dealing with Gradient Descent Based algorithms (Linear and Logistic Regression, Neural Network) and Distance-based algorithms (KNN, K-means, SVM) as these are very sensitive to the range of the data points.
4. What is a logistic function? What is the range of values of a logistic function?
Ans. f(z) = 1/(1+e -z )
The values of a logistic function will range from 0 to 1. The values of Z will vary from -infinity to +infinity.
5. What are the drawbacks of a linear model?
Ans. There are a couple of drawbacks of a linear model:
A linear model holds some strong assumptions that may not be true in application. It assumes a linear relationship, multivariate normality, no or little multicollinearity, no auto-correlation, and homoscedasticity
A linear model can’t be used for discrete or binary outcomes.
You can’t vary the model flexibility of a linear model.
❤2
Forwarded from Python Projects & Resources
𝟮𝟱+ 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗬𝗼𝘂𝗿 𝗗𝗿𝗲𝗮𝗺 𝗝𝗼𝗯 😍
Breaking into Data Analytics isn’t just about knowing the tools — it’s about answering the right questions with confidence🧑💻✨️
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Whether you’re aiming for your first role or looking to level up your career, these real interview questions will test your skills📊📌
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Don’t just learn — prepare smart✅️
❤1
Artificial Intelligence (AI) is the simulation of human intelligence in machines that are designed to think, learn, and make decisions. From virtual assistants to self-driving cars, AI is transforming how we interact with technology.
Hers is the brief A-Z overview of the terms used in Artificial Intelligence World
A - Algorithm: A set of rules or instructions that an AI system follows to solve problems or make decisions.
B - Bias: Prejudice in AI systems due to skewed training data, leading to unfair outcomes.
C - Chatbot: AI software that can hold conversations with users via text or voice.
D - Deep Learning: A type of machine learning using layered neural networks to analyze data and make decisions.
E - Expert System: An AI that replicates the decision-making ability of a human expert in a specific domain.
F - Fine-Tuning: The process of refining a pre-trained model on a specific task or dataset.
G - Generative AI: AI that can create new content like text, images, audio, or code.
H - Heuristic: A rule-of-thumb or shortcut used by AI to make decisions efficiently.
I - Image Recognition: The ability of AI to detect and classify objects or features in an image.
J - Jupyter Notebook: A tool widely used in AI for interactive coding, data visualization, and documentation.
K - Knowledge Representation: How AI systems store, organize, and use information for reasoning.
L - LLM (Large Language Model): An AI trained on large text datasets to understand and generate human language (e.g., GPT-4).
M - Machine Learning: A branch of AI where systems learn from data instead of being explicitly programmed.
N - NLP (Natural Language Processing): AI's ability to understand, interpret, and generate human language.
O - Overfitting: When a model performs well on training data but poorly on unseen data due to memorizing instead of generalizing.
P - Prompt Engineering: Crafting effective inputs to steer generative AI toward desired responses.
Q - Q-Learning: A reinforcement learning algorithm that helps agents learn the best actions to take.
R - Reinforcement Learning: A type of learning where AI agents learn by interacting with environments and receiving rewards.
S - Supervised Learning: Machine learning where models are trained on labeled datasets.
T - Transformer: A neural network architecture powering models like GPT and BERT, crucial in NLP tasks.
U - Unsupervised Learning: A method where AI finds patterns in data without labeled outcomes.
V - Vision (Computer Vision): The field of AI that enables machines to interpret and process visual data.
W - Weak AI: AI designed to handle narrow tasks without consciousness or general intelligence.
X - Explainable AI (XAI): Techniques that make AI decision-making transparent and understandable to humans.
Y - YOLO (You Only Look Once): A popular real-time object detection algorithm in computer vision.
Z - Zero-shot Learning: The ability of AI to perform tasks it hasn’t been explicitly trained on.
Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Hers is the brief A-Z overview of the terms used in Artificial Intelligence World
A - Algorithm: A set of rules or instructions that an AI system follows to solve problems or make decisions.
B - Bias: Prejudice in AI systems due to skewed training data, leading to unfair outcomes.
C - Chatbot: AI software that can hold conversations with users via text or voice.
D - Deep Learning: A type of machine learning using layered neural networks to analyze data and make decisions.
E - Expert System: An AI that replicates the decision-making ability of a human expert in a specific domain.
F - Fine-Tuning: The process of refining a pre-trained model on a specific task or dataset.
G - Generative AI: AI that can create new content like text, images, audio, or code.
H - Heuristic: A rule-of-thumb or shortcut used by AI to make decisions efficiently.
I - Image Recognition: The ability of AI to detect and classify objects or features in an image.
J - Jupyter Notebook: A tool widely used in AI for interactive coding, data visualization, and documentation.
K - Knowledge Representation: How AI systems store, organize, and use information for reasoning.
L - LLM (Large Language Model): An AI trained on large text datasets to understand and generate human language (e.g., GPT-4).
M - Machine Learning: A branch of AI where systems learn from data instead of being explicitly programmed.
N - NLP (Natural Language Processing): AI's ability to understand, interpret, and generate human language.
O - Overfitting: When a model performs well on training data but poorly on unseen data due to memorizing instead of generalizing.
P - Prompt Engineering: Crafting effective inputs to steer generative AI toward desired responses.
Q - Q-Learning: A reinforcement learning algorithm that helps agents learn the best actions to take.
R - Reinforcement Learning: A type of learning where AI agents learn by interacting with environments and receiving rewards.
S - Supervised Learning: Machine learning where models are trained on labeled datasets.
T - Transformer: A neural network architecture powering models like GPT and BERT, crucial in NLP tasks.
U - Unsupervised Learning: A method where AI finds patterns in data without labeled outcomes.
V - Vision (Computer Vision): The field of AI that enables machines to interpret and process visual data.
W - Weak AI: AI designed to handle narrow tasks without consciousness or general intelligence.
X - Explainable AI (XAI): Techniques that make AI decision-making transparent and understandable to humans.
Y - YOLO (You Only Look Once): A popular real-time object detection algorithm in computer vision.
Z - Zero-shot Learning: The ability of AI to perform tasks it hasn’t been explicitly trained on.
Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
❤3
Forwarded from Artificial Intelligence
𝐄𝐚𝐫𝐧 𝐅𝐑𝐄𝐄 𝐎𝐫𝐚𝐜𝐥𝐞 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝟐𝟎𝟐𝟓 — 𝐂𝐥𝐨𝐮𝐝, 𝐀𝐈 & 𝐃𝐚𝐭𝐚!😍
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💡 Choose from in-demand certifications in:
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Oracle’s Race to Certification is here — your chance to earn globally recognized certifications for FREE!💥
💡 Choose from in-demand certifications in:
☁️ Cloud
🤖 AI
📊 Data
…and more!
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4lx2tin
⚡But hurry — spots are limited, and the clock is ticking!✅️
❤2
Most people learn SQL just enough to pull some data. But if you really understand it, you can analyze massive datasets without touching Excel or Python.
Here are 8 game-changing SQL concepts that will make you a data pro:
👇
1. Stop pulling raw data. Start pulling insights.
The biggest mistake? Running a query that gives you everything and then filtering it later.
Good analysts don’t pull raw data. They shape the data before it even reaches them.
2. “SELECT ” is a rookie move.
Pulling all columns is lazy and slow.
A pro only selects what they need.
✔️ Fewer columns = Faster queries
✔️ Less noise = Clearer insights
The more precise your query, the less time you waste cleaning data.
3. GROUP BY is your best friend.
You don’t need 100,000 rows of transactions. What you need is:
✔️ Sales per region
✔️ Average order size per customer
✔️ Number of signups per month
Grouping turns chaotic data into useful summaries.
4. Joins = Connecting the dots.
Your most important data is split across multiple tables.
Want to know how much each customer spent? You need to join:
✔️ Customer info
✔️ Order history
✔️ Payments
Joins = unlocking hidden insights.
5. Window functions will blow your mind.
They let you:
✔️ Rank customers by total purchases
✔️ Calculate rolling averages
✔️ Compare each row to the overall trend
It’s like pivot tables, but way more powerful.
6. CTEs will save you from spaghetti SQL.
Instead of writing a 50-line nested query, break it into steps.
CTEs (Common Table Expressions) make your SQL:
✔️ Easier to read
✔️ Easier to debug
✔️ Reusable
Good SQL is clean SQL.
7. Indexes = Speed.
If your queries take forever, your database is probably doing unnecessary work.
Indexes help databases find data faster.
If you work with large datasets, this is a game changer.
SQL isn’t just about pulling data. It’s about analyzing, transforming, and optimizing it.
Master these 7 concepts, and you’ll never look at SQL the same way again.
Join us on WhatsApp: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Here are 8 game-changing SQL concepts that will make you a data pro:
👇
1. Stop pulling raw data. Start pulling insights.
The biggest mistake? Running a query that gives you everything and then filtering it later.
Good analysts don’t pull raw data. They shape the data before it even reaches them.
2. “SELECT ” is a rookie move.
Pulling all columns is lazy and slow.
A pro only selects what they need.
✔️ Fewer columns = Faster queries
✔️ Less noise = Clearer insights
The more precise your query, the less time you waste cleaning data.
3. GROUP BY is your best friend.
You don’t need 100,000 rows of transactions. What you need is:
✔️ Sales per region
✔️ Average order size per customer
✔️ Number of signups per month
Grouping turns chaotic data into useful summaries.
4. Joins = Connecting the dots.
Your most important data is split across multiple tables.
Want to know how much each customer spent? You need to join:
✔️ Customer info
✔️ Order history
✔️ Payments
Joins = unlocking hidden insights.
5. Window functions will blow your mind.
They let you:
✔️ Rank customers by total purchases
✔️ Calculate rolling averages
✔️ Compare each row to the overall trend
It’s like pivot tables, but way more powerful.
6. CTEs will save you from spaghetti SQL.
Instead of writing a 50-line nested query, break it into steps.
CTEs (Common Table Expressions) make your SQL:
✔️ Easier to read
✔️ Easier to debug
✔️ Reusable
Good SQL is clean SQL.
7. Indexes = Speed.
If your queries take forever, your database is probably doing unnecessary work.
Indexes help databases find data faster.
If you work with large datasets, this is a game changer.
SQL isn’t just about pulling data. It’s about analyzing, transforming, and optimizing it.
Master these 7 concepts, and you’ll never look at SQL the same way again.
Join us on WhatsApp: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
❤5
𝟯 𝗚𝗮𝗺𝗲-𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲😍
Want to break into Data Science or Tech?
Python is the #1 skill you need — and starting is easier than you think.🧑💻✨️
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3JemBIt
Your career upgrade starts today — no excuses!✅️
Want to break into Data Science or Tech?
Python is the #1 skill you need — and starting is easier than you think.🧑💻✨️
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3JemBIt
Your career upgrade starts today — no excuses!✅️
❤2
Forwarded from Python Projects & Resources
𝟒 𝐁𝐞𝐬𝐭 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 𝐢𝐧 𝟐𝟎𝟐𝟓 𝐭𝐨 𝐒𝐤𝐲𝐫𝐨𝐜𝐤𝐞𝐭 𝐘𝐨𝐮𝐫 𝐂𝐚𝐫𝐞𝐞𝐫😍
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In today’s data-driven world, Power BI has become one of the most in-demand tools for businesses〽️📊
The best part? You don’t need to spend a fortune—there are free and affordable courses available online to get you started.💥🧑💻
𝐋𝐢𝐧𝐤👇:-
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Start learning today and position yourself for success in 2025!✅️
❤1
📊 Data Science Project Ideas to Practice & Master Your Skills ✅
🟢 Beginner Level
• Titanic Survival Prediction (Logistic Regression)
• House Price Prediction (Linear Regression)
• Exploratory Data Analysis on IPL or Netflix Dataset
• Customer Segmentation (K-Means Clustering)
• Weather Data Visualization
🟡 Intermediate Level
• Sentiment Analysis on Tweets
• Credit Card Fraud Detection
• Time Series Forecasting (Stock or Sales Data)
• Image Classification using CNN (Fashion MNIST)
• Recommendation System for Movies/Products
🔴 Advanced Level
• End-to-End Machine Learning Pipeline with Deployment
• NLP Chatbot using Transformers
• Real-Time Dashboard with Streamlit + ML
• Anomaly Detection in Network Traffic
• A/B Testing & Business Decision Modeling
💬 Double Tap ❤️ for more! 🤖📈
🟢 Beginner Level
• Titanic Survival Prediction (Logistic Regression)
• House Price Prediction (Linear Regression)
• Exploratory Data Analysis on IPL or Netflix Dataset
• Customer Segmentation (K-Means Clustering)
• Weather Data Visualization
🟡 Intermediate Level
• Sentiment Analysis on Tweets
• Credit Card Fraud Detection
• Time Series Forecasting (Stock or Sales Data)
• Image Classification using CNN (Fashion MNIST)
• Recommendation System for Movies/Products
🔴 Advanced Level
• End-to-End Machine Learning Pipeline with Deployment
• NLP Chatbot using Transformers
• Real-Time Dashboard with Streamlit + ML
• Anomaly Detection in Network Traffic
• A/B Testing & Business Decision Modeling
💬 Double Tap ❤️ for more! 🤖📈
❤6