Python Projects
Face Recognition Python Project: Click Here!
How to make Jarvis in Python: Click Here!
Python Chatbot Tutorial: Click Here!
Simple Student Result Database System: Click Here!
Don't Forget to like & share it with your friends.
Face Recognition Python Project: Click Here!
How to make Jarvis in Python: Click Here!
Python Chatbot Tutorial: Click Here!
Simple Student Result Database System: Click Here!
Don't Forget to like & share it with your friends.
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Python Constructs
1. Functions in Python
A function in Python is a collection of statements grouped under a name. You can use it whenever you want to execute all those statements at a time.
You can call it wherever you want and as many times as you want in a program. A function may return a value.
2. Classes in Python
Python is an object-oriented language. It supports classes and objects.
A class is an abstract data type. In other words, it is a blueprint for an object of a certain kind. It holds no values.
An object is a real-world entity and an instance of a class.
3. Modules in Python
Python module is a collection of related classes and functions.
We have modules for mathematical calculations, string manipulations, web programming, and many more.
4. Packages in Python
Python package is a collection of related modules. You can either import a package or create your own.
Python has a lot of other constructs. These include control structures, functions, exceptions, etc
1. Functions in Python
A function in Python is a collection of statements grouped under a name. You can use it whenever you want to execute all those statements at a time.
You can call it wherever you want and as many times as you want in a program. A function may return a value.
2. Classes in Python
Python is an object-oriented language. It supports classes and objects.
A class is an abstract data type. In other words, it is a blueprint for an object of a certain kind. It holds no values.
An object is a real-world entity and an instance of a class.
3. Modules in Python
Python module is a collection of related classes and functions.
We have modules for mathematical calculations, string manipulations, web programming, and many more.
4. Packages in Python
Python package is a collection of related modules. You can either import a package or create your own.
Python has a lot of other constructs. These include control structures, functions, exceptions, etc
👉 What is Python Data Structures?
You can think of a data structure as a way of organizing and storing data such that we can access and modify it efficiently.
We have primitive data types like integers, floats, Booleans, and strings.
👉 What is Python List?
A list in Python is a heterogeneous container for items. This would remind you of an array in C++, but since Python does not support arrays, we have Python Lists.
👉 Python Tuple
This Python Data Structure is like a, like a list in Python, is a heterogeneous container for items.
But the major difference between the two (tuple and list) is that a list is mutable, but a tuple is immutable.
This means that while you can reassign or delete an entire tuple, you cannot do the same to a single item or a slice.
👉 Python Dictionaries
Finally, we will take a look at Python dictionaries. Think of a real-life dictionary. What is it used for? It holds word-meaning pairs. Likewise, a Python dictionary holds key-value pairs. However, you may not use an unhashable item as a key.
To declare a Python dictionary, we use curly braces. But since it has key-value pairs instead of single values, this differentiates a dictionary from a set.
You can think of a data structure as a way of organizing and storing data such that we can access and modify it efficiently.
We have primitive data types like integers, floats, Booleans, and strings.
👉 What is Python List?
A list in Python is a heterogeneous container for items. This would remind you of an array in C++, but since Python does not support arrays, we have Python Lists.
👉 Python Tuple
This Python Data Structure is like a, like a list in Python, is a heterogeneous container for items.
But the major difference between the two (tuple and list) is that a list is mutable, but a tuple is immutable.
This means that while you can reassign or delete an entire tuple, you cannot do the same to a single item or a slice.
👉 Python Dictionaries
Finally, we will take a look at Python dictionaries. Think of a real-life dictionary. What is it used for? It holds word-meaning pairs. Likewise, a Python dictionary holds key-value pairs. However, you may not use an unhashable item as a key.
To declare a Python dictionary, we use curly braces. But since it has key-value pairs instead of single values, this differentiates a dictionary from a set.
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What is Python Loop?
When you want some statements to execute a hundred times, you don’t repeat them 100 times.
Think of when you want to print numbers 1 to 99. Or that you want to say Hello to 99 friends.
In such a case, you can use loops in python.
Here, we will discuss 4 types of Python Loop:
Python For Loop
Python While Loop
Python Loop Control Statements
Nested For Loop in Python
Python While Loop
A while loop in python iterates till its condition becomes False. In other words, it executes the statements under itself while the condition it takes is True.
Python For Loop
Python for loop can iterate over a sequence of items. The structure of a for loop in Python is different than that in C++ or Java.
That is, for(int i=0;i<n;i++) won’t work here. In Python, we use the ‘in’ keyword.
Nested for Loops in Python
You can also nest a loop inside another. You can put a for loop inside a while, or a while inside a for, or a for inside a for, or a while inside a while.
Or you can put a loop inside a loop inside a loop. You can go as far as you want.
Loop Control Statements in Python
Sometimes, you may want to break out of normal execution in a loop.
For this, we have three keywords in Python- break, continue, and Python
When you want some statements to execute a hundred times, you don’t repeat them 100 times.
Think of when you want to print numbers 1 to 99. Or that you want to say Hello to 99 friends.
In such a case, you can use loops in python.
Here, we will discuss 4 types of Python Loop:
Python For Loop
Python While Loop
Python Loop Control Statements
Nested For Loop in Python
Python While Loop
A while loop in python iterates till its condition becomes False. In other words, it executes the statements under itself while the condition it takes is True.
Python For Loop
Python for loop can iterate over a sequence of items. The structure of a for loop in Python is different than that in C++ or Java.
That is, for(int i=0;i<n;i++) won’t work here. In Python, we use the ‘in’ keyword.
Nested for Loops in Python
You can also nest a loop inside another. You can put a for loop inside a while, or a while inside a for, or a for inside a for, or a while inside a while.
Or you can put a loop inside a loop inside a loop. You can go as far as you want.
Loop Control Statements in Python
Sometimes, you may want to break out of normal execution in a loop.
For this, we have three keywords in Python- break, continue, and Python
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💡 Tips to Crack Top Tech Companies using LeetCode 💻
Are you aiming to crack top tech companies? Here are some tips on how to effectively use the LeetCode platform to enhance your problem-solving skills and increase your chances of success:
1️⃣ Quality > Quantity ✅
Rather than focusing on solving a large number of problems, prioritize the quality of your solutions. It's better to solve a particular Data Structures and Algorithms (DSA) sheet thoroughly and revise it until you can build up the logic on your own. Consider using resources like the Striver Sheet or Grind 75 to guide your practice.
2️⃣ Maintain an Error Sheet ✅
Create an error sheet to keep track of the questions you've solved and the mistakes you've made while solving them. By reviewing this sheet regularly, you can identify common errors and strive to avoid repeating them. This practice will significantly improve your problem-solving skills over time.
3️⃣ Solve Top Interview Questions ✅
When preparing for a specific company's interview, focus on solving recent LeetCode questions that are tagged with that company's name. This way, you'll be familiar with the types of problems the company typically asks and be better prepared for the interview.
4️⃣ For Beginners ✅
If you're new to problem-solving, it's advisable to start with Easy-level problems before moving on to Medium or Hard ones. Aim to solve at least 25 problems in the Easy category before challenging yourself with more complex ones. This approach helps build a strong foundation and boosts your confidence.
5️⃣ Practice Weak Topics Regularly ✅
Identify the topics or problem types that you find challenging or fear the most. For example, if you struggle with graph problems, make it a habit to solve at least one graph problem every day. Regular practice in your weaker areas will help you improve your skills and boost your overall performance.
6️⃣ Don't Ignore Acceptance Level ✅
When browsing problems on LeetCode, consider sorting them by acceptance level. Prioritizing problems with a higher acceptance rate increases the likelihood of successfully solving them. This strategy allows you to build confidence by tackling problems that have been well-received by other users.
7️⃣ Don't Ignore Other Solutions ✅
Even if your solution is correct and accepted, don't overlook the opportunity to learn from others. Explore alternative solutions to the same problem. This practice exposes you to different approaches, algorithms, and optimizations, enabling you to discover new and efficient ways of solving problems.
8️⃣ Consistency is the Key ✅
Maintain a consistent practice schedule to make steady progress. Dedicate a block of time, such as 2-3 hours each day, to solve LeetCode problems. Additionally, set aside a specific day, like Saturdays, for weekly revisions. Consistency and regular practice will sharpen your problem-solving skills and increase your chances of cracking top tech company interviews.
Good luck with your LeetCode journey! 🚀
Are you aiming to crack top tech companies? Here are some tips on how to effectively use the LeetCode platform to enhance your problem-solving skills and increase your chances of success:
1️⃣ Quality > Quantity ✅
Rather than focusing on solving a large number of problems, prioritize the quality of your solutions. It's better to solve a particular Data Structures and Algorithms (DSA) sheet thoroughly and revise it until you can build up the logic on your own. Consider using resources like the Striver Sheet or Grind 75 to guide your practice.
2️⃣ Maintain an Error Sheet ✅
Create an error sheet to keep track of the questions you've solved and the mistakes you've made while solving them. By reviewing this sheet regularly, you can identify common errors and strive to avoid repeating them. This practice will significantly improve your problem-solving skills over time.
3️⃣ Solve Top Interview Questions ✅
When preparing for a specific company's interview, focus on solving recent LeetCode questions that are tagged with that company's name. This way, you'll be familiar with the types of problems the company typically asks and be better prepared for the interview.
4️⃣ For Beginners ✅
If you're new to problem-solving, it's advisable to start with Easy-level problems before moving on to Medium or Hard ones. Aim to solve at least 25 problems in the Easy category before challenging yourself with more complex ones. This approach helps build a strong foundation and boosts your confidence.
5️⃣ Practice Weak Topics Regularly ✅
Identify the topics or problem types that you find challenging or fear the most. For example, if you struggle with graph problems, make it a habit to solve at least one graph problem every day. Regular practice in your weaker areas will help you improve your skills and boost your overall performance.
6️⃣ Don't Ignore Acceptance Level ✅
When browsing problems on LeetCode, consider sorting them by acceptance level. Prioritizing problems with a higher acceptance rate increases the likelihood of successfully solving them. This strategy allows you to build confidence by tackling problems that have been well-received by other users.
7️⃣ Don't Ignore Other Solutions ✅
Even if your solution is correct and accepted, don't overlook the opportunity to learn from others. Explore alternative solutions to the same problem. This practice exposes you to different approaches, algorithms, and optimizations, enabling you to discover new and efficient ways of solving problems.
8️⃣ Consistency is the Key ✅
Maintain a consistent practice schedule to make steady progress. Dedicate a block of time, such as 2-3 hours each day, to solve LeetCode problems. Additionally, set aside a specific day, like Saturdays, for weekly revisions. Consistency and regular practice will sharpen your problem-solving skills and increase your chances of cracking top tech company interviews.
Good luck with your LeetCode journey! 🚀
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Python Roadmap for 2025: Complete Guide
1. Python Fundamentals
1.1 Variables, constants, and comments.
1.2 Data types: int, float, str, bool, complex.
1.3 Input and output (input(), print(), formatted strings).
1.4 Python syntax: Indentation and code structure.
2. Operators
2.1 Arithmetic: +, -, *, /, %, //, **.
2.2 Comparison: ==, !=, <, >, <=, >=.
2.3 Logical: and, or, not.
2.4 Bitwise: &, |, ^, ~, <<, >>.
2.5 Identity: is, is not.
2.6 Membership: in, not in.
3. Control Flow
3.1 Conditional statements: if, elif, else.
3.2 Loops: for, while.
3.3 Loop control: break, continue, pass.
4. Data Structures
4.1 Lists: Indexing, slicing, methods (append(), pop(), sort(), etc.).
4.2 Tuples: Immutability, packing/unpacking.
4.3 Dictionaries: Key-value pairs, methods (get(), items(), etc.).
4.4 Sets: Unique elements, set operations (union, intersection).
4.5 Strings: Immutability, methods (split(), strip(), replace()).
5. Functions
5.1 Defining functions with def.
5.2 Arguments: Positional, keyword, default, *args, **kwargs.
5.3 Anonymous functions (lambda).
5.4 Recursion.
6. Modules and Packages
6.1 Importing: import, from ... import.
6.2 Standard libraries: math, os, sys, random, datetime, time.
6.3 Installing external libraries with pip.
7. File Handling
7.1 Open and close files (open(), close()).
7.2 Read and write (read(), write(), readlines()).
7.3 Using context managers (with open(...)).
8. Object-Oriented Programming (OOP)
8.1 Classes and objects.
8.2 Methods and attributes.
8.3 Constructor (init).
8.4 Inheritance, polymorphism, encapsulation.
8.5 Special methods (str, repr, etc.).
9. Error and Exception Handling
9.1 try, except, else, finally.
9.2 Raising exceptions (raise).
9.3 Custom exceptions.
10. Comprehensions
10.1 List comprehensions.
10.2 Dictionary comprehensions.
10.3 Set comprehensions.
11. Iterators and Generators
11.1 Creating iterators using iter() and next().
11.2 Generators with yield.
11.3 Generator expressions.
12. Decorators and Closures
12.1 Functions as first-class citizens.
12.2 Nested functions.
12.3 Closures.
12.4 Creating and applying decorators.
13. Advanced Topics
13.1 Context managers (with statement).
13.2 Multithreading and multiprocessing.
13.3 Asynchronous programming with async and await.
13.4 Python's Global Interpreter Lock (GIL).
14. Python Internals
14.1 Mutable vs immutable objects.
14.2 Memory management and garbage collection.
14.3 Python's name == "main" mechanism.
15. Libraries and Frameworks
15.1 Data Science: NumPy, Pandas, Matplotlib, Seaborn.
15.2 Web Development: Flask, Django, FastAPI.
15.3 Testing: unittest, pytest.
15.4 APIs: requests, http.client.
15.5 Automation: selenium, os.
15.6 Machine Learning: scikit-learn, TensorFlow, PyTorch.
16. Tools and Best Practices
16.1 Debugging: pdb, breakpoints.
16.2 Code style: PEP 8 guidelines.
16.3 Virtual environments: venv.
16.4 Version control: Git + GitHub.
👇 Python Interview 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀
https://news.1rj.ru/str/dsabooks
📘 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 : https://topmate.io/coding/914624
📙 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
Join What's app channel for jobs updates: t.me/getjobss
1. Python Fundamentals
1.1 Variables, constants, and comments.
1.2 Data types: int, float, str, bool, complex.
1.3 Input and output (input(), print(), formatted strings).
1.4 Python syntax: Indentation and code structure.
2. Operators
2.1 Arithmetic: +, -, *, /, %, //, **.
2.2 Comparison: ==, !=, <, >, <=, >=.
2.3 Logical: and, or, not.
2.4 Bitwise: &, |, ^, ~, <<, >>.
2.5 Identity: is, is not.
2.6 Membership: in, not in.
3. Control Flow
3.1 Conditional statements: if, elif, else.
3.2 Loops: for, while.
3.3 Loop control: break, continue, pass.
4. Data Structures
4.1 Lists: Indexing, slicing, methods (append(), pop(), sort(), etc.).
4.2 Tuples: Immutability, packing/unpacking.
4.3 Dictionaries: Key-value pairs, methods (get(), items(), etc.).
4.4 Sets: Unique elements, set operations (union, intersection).
4.5 Strings: Immutability, methods (split(), strip(), replace()).
5. Functions
5.1 Defining functions with def.
5.2 Arguments: Positional, keyword, default, *args, **kwargs.
5.3 Anonymous functions (lambda).
5.4 Recursion.
6. Modules and Packages
6.1 Importing: import, from ... import.
6.2 Standard libraries: math, os, sys, random, datetime, time.
6.3 Installing external libraries with pip.
7. File Handling
7.1 Open and close files (open(), close()).
7.2 Read and write (read(), write(), readlines()).
7.3 Using context managers (with open(...)).
8. Object-Oriented Programming (OOP)
8.1 Classes and objects.
8.2 Methods and attributes.
8.3 Constructor (init).
8.4 Inheritance, polymorphism, encapsulation.
8.5 Special methods (str, repr, etc.).
9. Error and Exception Handling
9.1 try, except, else, finally.
9.2 Raising exceptions (raise).
9.3 Custom exceptions.
10. Comprehensions
10.1 List comprehensions.
10.2 Dictionary comprehensions.
10.3 Set comprehensions.
11. Iterators and Generators
11.1 Creating iterators using iter() and next().
11.2 Generators with yield.
11.3 Generator expressions.
12. Decorators and Closures
12.1 Functions as first-class citizens.
12.2 Nested functions.
12.3 Closures.
12.4 Creating and applying decorators.
13. Advanced Topics
13.1 Context managers (with statement).
13.2 Multithreading and multiprocessing.
13.3 Asynchronous programming with async and await.
13.4 Python's Global Interpreter Lock (GIL).
14. Python Internals
14.1 Mutable vs immutable objects.
14.2 Memory management and garbage collection.
14.3 Python's name == "main" mechanism.
15. Libraries and Frameworks
15.1 Data Science: NumPy, Pandas, Matplotlib, Seaborn.
15.2 Web Development: Flask, Django, FastAPI.
15.3 Testing: unittest, pytest.
15.4 APIs: requests, http.client.
15.5 Automation: selenium, os.
15.6 Machine Learning: scikit-learn, TensorFlow, PyTorch.
16. Tools and Best Practices
16.1 Debugging: pdb, breakpoints.
16.2 Code style: PEP 8 guidelines.
16.3 Virtual environments: venv.
16.4 Version control: Git + GitHub.
👇 Python Interview 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀
https://news.1rj.ru/str/dsabooks
📘 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 : https://topmate.io/coding/914624
📙 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
Join What's app channel for jobs updates: t.me/getjobss
👍5❤1
🚀 Python Trick: Squaring List Elements 🚀
🔴 Less Efficient Method:
def square(n):
return n * n
List = [1, 2, 3, 4, 5]
squares = []
for i in List:
squares.append(square(i))
print(squares)
Output: [1, 4, 9, 16, 25]
🟢 More Efficient Method:
def square(n):
return n * n
List = [1, 2, 3, 4, 5]
squares = list(map(square, List))
print(squares)
Output: [1, 4, 9, 16, 25]
Using map() , we can simplify the code and improve readability! This method is not only concise but also more Pythonic.
Credits: https://news.1rj.ru/str/Programming_experts/1442
🔴 Less Efficient Method:
def square(n):
return n * n
List = [1, 2, 3, 4, 5]
squares = []
for i in List:
squares.append(square(i))
print(squares)
Output: [1, 4, 9, 16, 25]
🟢 More Efficient Method:
def square(n):
return n * n
List = [1, 2, 3, 4, 5]
squares = list(map(square, List))
print(squares)
Output: [1, 4, 9, 16, 25]
Using map() , we can simplify the code and improve readability! This method is not only concise but also more Pythonic.
Credits: https://news.1rj.ru/str/Programming_experts/1442
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A-Z of essential data science concepts
A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content 😄👍
Hope this helps you 😊
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