Forwarded from Python Projects & Resources
๐๐ญ๐๐ซ๐ญ ๐๐จ๐ฎ๐ซ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ญ๐ข๐๐ฌ ๐๐จ๐ฎ๐ซ๐ง๐๐ฒ โ ๐๐๐% ๐
๐ซ๐๐ & ๐๐๐ ๐ข๐ง๐ง๐๐ซ-๐
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Want to dive into data analytics but donโt know where to start?๐งโ๐ปโจ๏ธ
These free Microsoft learning paths take you from analytics basics to creating dashboards, AI insights with Copilot, and end-to-end analytics with Microsoft Fabric.๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/47oQD6f
No prior experience needed โ just curiosityโ ๏ธ
Want to dive into data analytics but donโt know where to start?๐งโ๐ปโจ๏ธ
These free Microsoft learning paths take you from analytics basics to creating dashboards, AI insights with Copilot, and end-to-end analytics with Microsoft Fabric.๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/47oQD6f
No prior experience needed โ just curiosityโ ๏ธ
โค2
1.What are the conditions for Overfitting and Underfitting?
Ans:
โข 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.
โข Overfitting โ Low bias and High Variance results in the overfitted model. The decision tree is more prone to Overfitting.
โข Underfitting โ High bias and Low Variance. Such a model doesnโt perform well on test data also. For example โ 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 โ 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 canโt be used for discrete or binary outcomes.
You canโt vary the model flexibility of a linear model.
Ans:
โข 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.
โข Overfitting โ Low bias and High Variance results in the overfitted model. The decision tree is more prone to Overfitting.
โข Underfitting โ High bias and Low Variance. Such a model doesnโt perform well on test data also. For example โ 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 โ 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 canโt be used for discrete or binary outcomes.
You canโt vary the model flexibility of a linear model.
โค2
Forwarded from Python Projects & Resources
๐ฎ๐ฑ+ ๐ ๐๐๐-๐๐ป๐ผ๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐ ๐๐ผ ๐๐ฎ๐ป๐ฑ ๐ฌ๐ผ๐๐ฟ ๐๐ฟ๐ฒ๐ฎ๐บ ๐๐ผ๐ฏ ๐
Breaking into Data Analytics isnโt just about knowing the tools โ itโs about answering the right questions with confidence๐งโ๐ปโจ๏ธ
Whether youโre aiming for your first role or looking to level up your career, these real interview questions will test your skills๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3JumloI
Donโt just learn โ prepare smartโ ๏ธ
Breaking into Data Analytics isnโt just about knowing the tools โ itโs about answering the right questions with confidence๐งโ๐ปโจ๏ธ
Whether youโre aiming for your first role or looking to level up your career, these real interview questions will test your skills๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3JumloI
Donโt just learn โ prepare smartโ ๏ธ
โค1
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 hasnโt 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 hasnโt been explicitly trained on.
Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
โค2
Forwarded from Artificial Intelligence
๐๐๐ซ๐ง ๐
๐๐๐ ๐๐ซ๐๐๐ฅ๐ ๐๐๐ซ๐ญ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง๐ฌ ๐ข๐ง ๐๐๐๐ โ ๐๐ฅ๐จ๐ฎ๐, ๐๐ & ๐๐๐ญ๐!๐
Oracleโs Race to Certification is here โ your chance to earn globally recognized certifications for FREE!๐ฅ
๐ก Choose from in-demand certifications in:
โ๏ธ Cloud
๐ค AI
๐ Data
โฆand more!
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4lx2tin
โกBut hurry โ spots are limited, and the clock is ticking!โ ๏ธ
Oracleโs Race to Certification is here โ your chance to earn globally recognized certifications for FREE!๐ฅ
๐ก Choose from in-demand certifications in:
โ๏ธ Cloud
๐ค AI
๐ Data
โฆand more!
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4lx2tin
โกBut hurry โ spots are limited, and the clock is ticking!โ ๏ธ
โค2
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:
๐
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 donโt pull raw data. They shape the data before it even reaches them.
2. โSELECT โ is a rookie move.
Pulling all columns is lazy and slow.
A pro only selects what they need.
โ๏ธ Fewer columns = Faster queries
โ๏ธ Less noise = Clearer insights
The more precise your query, the less time you waste cleaning data.
3. GROUP BY is your best friend.
You donโt need 100,000 rows of transactions. What you need is:
โ๏ธ Sales per region
โ๏ธ Average order size per customer
โ๏ธ 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:
โ๏ธ Customer info
โ๏ธ Order history
โ๏ธ Payments
Joins = unlocking hidden insights.
5. Window functions will blow your mind.
They let you:
โ๏ธ Rank customers by total purchases
โ๏ธ Calculate rolling averages
โ๏ธ Compare each row to the overall trend
Itโs 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:
โ๏ธ Easier to read
โ๏ธ Easier to debug
โ๏ธ 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 isnโt just about pulling data. Itโs about analyzing, transforming, and optimizing it.
Master these 7 concepts, and youโll 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:
๐
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 donโt pull raw data. They shape the data before it even reaches them.
2. โSELECT โ is a rookie move.
Pulling all columns is lazy and slow.
A pro only selects what they need.
โ๏ธ Fewer columns = Faster queries
โ๏ธ Less noise = Clearer insights
The more precise your query, the less time you waste cleaning data.
3. GROUP BY is your best friend.
You donโt need 100,000 rows of transactions. What you need is:
โ๏ธ Sales per region
โ๏ธ Average order size per customer
โ๏ธ 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:
โ๏ธ Customer info
โ๏ธ Order history
โ๏ธ Payments
Joins = unlocking hidden insights.
5. Window functions will blow your mind.
They let you:
โ๏ธ Rank customers by total purchases
โ๏ธ Calculate rolling averages
โ๏ธ Compare each row to the overall trend
Itโs 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:
โ๏ธ Easier to read
โ๏ธ Easier to debug
โ๏ธ 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 isnโt just about pulling data. Itโs about analyzing, transforming, and optimizing it.
Master these 7 concepts, and youโll never look at SQL the same way again.
Join us on WhatsApp: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
โค5
๐ฏ ๐๐ฎ๐บ๐ฒ-๐๐ต๐ฎ๐ป๐ด๐ถ๐ป๐ด ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐ฃ๐๐๐ต๐ผ๐ป ๐ณ๐ผ๐ฟ ๐๐ฟ๐ฒ๐ฒ๐
Want to break into Data Science or Tech?
Python is the #1 skill you need โ and starting is easier than you think.๐งโ๐ปโจ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3JemBIt
Your career upgrade starts today โ no excuses!โ ๏ธ
Want to break into Data Science or Tech?
Python is the #1 skill you need โ and starting is easier than you think.๐งโ๐ปโจ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3JemBIt
Your career upgrade starts today โ no excuses!โ ๏ธ
โค2