๐ณ What is a Decision Tree? ๐ณ
Imagine you're trying to figure out what to eat for dinner. ๐๐ฅ๐ A decision tree is like a flowchart that helps you make choices based on yes/no questions:
Are you in the mood for something light?
Yes โก๏ธ Salad ๐ฅ
No โก๏ธ Are you craving something cheesy?
Yes โก๏ธ Pizza ๐
No โก๏ธ Burger ๐
That's the essence of how decision trees work in machine learning!
๐ค In Machine Learning Terms:
Nodes: Questions (e.g., Is the price > $50?)
Branches: Possible answers (e.g., Yes/No)
Leaves: Final decisions or predictions (e.g., "Expensive" or "Affordable")
๐ They're used for tasks like:
โ Classifying emails as spam or not.
โ Predicting if a customer will buy a product.
โ Diagnosing diseases in healthcare.
๐ฏ Why are they Awesome?
Simple to understand (even for non-techies).
Visual and interpretable (you can see the logic behind predictions).
Great for small-to-medium datasets.
โก๏ธ Limitations:
They can "overfit" (become too specific).
Not the best for very large datasets or complex problems.
๐ Pro Tip:
To handle overfitting, use Random Forests ๐ฒ๐ฒ or Gradient Boosted Trees ๐โadvanced versions of decision trees.
What do you think about decision trees? Drop your ๐ณ below if you love their simplicity!
Imagine you're trying to figure out what to eat for dinner. ๐๐ฅ๐ A decision tree is like a flowchart that helps you make choices based on yes/no questions:
Are you in the mood for something light?
Yes โก๏ธ Salad ๐ฅ
No โก๏ธ Are you craving something cheesy?
Yes โก๏ธ Pizza ๐
No โก๏ธ Burger ๐
That's the essence of how decision trees work in machine learning!
๐ค In Machine Learning Terms:
Nodes: Questions (e.g., Is the price > $50?)
Branches: Possible answers (e.g., Yes/No)
Leaves: Final decisions or predictions (e.g., "Expensive" or "Affordable")
๐ They're used for tasks like:
โ Classifying emails as spam or not.
โ Predicting if a customer will buy a product.
โ Diagnosing diseases in healthcare.
๐ฏ Why are they Awesome?
Simple to understand (even for non-techies).
Visual and interpretable (you can see the logic behind predictions).
Great for small-to-medium datasets.
โก๏ธ Limitations:
They can "overfit" (become too specific).
Not the best for very large datasets or complex problems.
๐ Pro Tip:
To handle overfitting, use Random Forests ๐ฒ๐ฒ or Gradient Boosted Trees ๐โadvanced versions of decision trees.
What do you think about decision trees? Drop your ๐ณ below if you love their simplicity!
Begin to Use Cloud Computing with Anaconda Cloud Notebook
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Begin to use Cloud Computing and Anaconda Cloud Notebook with Python, Data Science and Machine Learning [2024]
Rating โญ๏ธ: 4.9 out 5
Students ๐จโ๐ : 1,028
Duration โฐ : 40min on-demand video
Created by ๐จโ๐ซ: Henrik Johansson
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common data analysis and machine learning tasks using python
Creator: Ujjwal Karn
Stars โญ๏ธ: 5.3k
Forked By: 1.5k
GithubRepo: https://github.com/ujjwalkarn/DataSciencePython
#datascience #python
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Join @datascience_bds for more cool repositories.
*This channel belongs to @bigdataspecialist group
GitHub
GitHub - ujjwalkarn/DataSciencePython: common data analysis and machine learning tasks using python
common data analysis and machine learning tasks using python - ujjwalkarn/DataSciencePython
Python for Deep Learning: Build Neural Networks in Python
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๐๐๐๐ญ๐จ๐ซ ๐๐๐ญ๐๐๐๐ฌ๐๐ฌ vs ๐๐ซ๐๐ฉ๐ก ๐๐๐ญ๐๐๐๐ฌ๐๐ฌ
Selecting the right database depends on your data needsโvector databases excel in similarity searches and embeddings, while graph databases are best for managing complex relationships between entities.
๐๐๐๐ญ๐จ๐ซ ๐๐๐ญ๐๐๐๐ฌ๐๐ฌ:
- Data Encoding: Vector databases encode data into vectors, which are numerical representations of the data.
- Partitioning and Indexing: Data is partitioned into chunks and encoded into vectors, which are then indexed for efficient retrieval.
- Ideal Use Cases: Perfect for tasks involving embedding representations, such as image recognition, natural language processing, and recommendation systems.
- Nearest Neighbor Searches: They excel in performing nearest neighbor searches, finding the most similar data points to a given query efficiently.
- Efficiency: The indexing of vectors enables fast and accurate information retrieval, making these databases suitable for high-dimensional data.
๐๐ซ๐๐ฉ๐ก ๐๐๐ญ๐๐๐๐ฌ๐๐ฌ:
- Relational Information Management: Graph databases are designed to handle and query relational information between entities.
- Node and Edge Representation: Entities are represented as nodes, and relationships between them as edges, allowing for intricate data modeling.
- Complex Relationships: They excel in scenarios where understanding and navigating complex relationships between data points is crucial.
- Knowledge Extraction: By indexing the resulting knowledge base, they can efficiently extract sub-knowledge bases, helping users focus on specific entities or relationships.
- Use Cases: Ideal for applications like social networks, fraud detection, and knowledge graphs where relationships and connections are the primary focus.
๐๐จ๐ง๐๐ฅ๐ฎ๐ฌ๐ข๐จ๐ง:
Choosing between a vector and a graph database depends on the nature of your data and the type of queries you need to perform. Vector databases are the go-to choice for tasks requiring similarity searches and embedding representations, while graph databases are indispensable for managing and querying complex relationships.
Source: Ashish Joshi
Selecting the right database depends on your data needsโvector databases excel in similarity searches and embeddings, while graph databases are best for managing complex relationships between entities.
๐๐๐๐ญ๐จ๐ซ ๐๐๐ญ๐๐๐๐ฌ๐๐ฌ:
- Data Encoding: Vector databases encode data into vectors, which are numerical representations of the data.
- Partitioning and Indexing: Data is partitioned into chunks and encoded into vectors, which are then indexed for efficient retrieval.
- Ideal Use Cases: Perfect for tasks involving embedding representations, such as image recognition, natural language processing, and recommendation systems.
- Nearest Neighbor Searches: They excel in performing nearest neighbor searches, finding the most similar data points to a given query efficiently.
- Efficiency: The indexing of vectors enables fast and accurate information retrieval, making these databases suitable for high-dimensional data.
๐๐ซ๐๐ฉ๐ก ๐๐๐ญ๐๐๐๐ฌ๐๐ฌ:
- Relational Information Management: Graph databases are designed to handle and query relational information between entities.
- Node and Edge Representation: Entities are represented as nodes, and relationships between them as edges, allowing for intricate data modeling.
- Complex Relationships: They excel in scenarios where understanding and navigating complex relationships between data points is crucial.
- Knowledge Extraction: By indexing the resulting knowledge base, they can efficiently extract sub-knowledge bases, helping users focus on specific entities or relationships.
- Use Cases: Ideal for applications like social networks, fraud detection, and knowledge graphs where relationships and connections are the primary focus.
๐๐จ๐ง๐๐ฅ๐ฎ๐ฌ๐ข๐จ๐ง:
Choosing between a vector and a graph database depends on the nature of your data and the type of queries you need to perform. Vector databases are the go-to choice for tasks requiring similarity searches and embedding representations, while graph databases are indispensable for managing and querying complex relationships.
Source: Ashish Joshi
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โญโญโญโญ๐TIME STAMP IS IN THE COMMENTS SECTION๐โญโญโญโญโญ
What you'll learn
โ Master the most up-to-date practical skills and knowledge that data scientists use in their daily roles
โ Learn the tools, languages, and libraries used by professional data scientists, includingโฆ
What you'll learn
โ Master the most up-to-date practical skills and knowledge that data scientists use in their daily roles
โ Learn the tools, languages, and libraries used by professional data scientists, includingโฆ
Screenshot_12.png
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๐ฅ ๐๐๐ญ๐ ๐๐ญ๐ซ๐ฎ๐๐ญ๐ฎ๐ซ๐๐ฌ ๐๐ข๐ฆ๐ฉ๐ฅ๐ข๐๐ข๐๐! ๐ฅ
๐ 1. Array โ Fixed-size collection of elements, perfect for fast lookups!
๐ฆ 2. Queue โ First in, first out (FIFO). Think of a line at a grocery store!
๐ณ 3. Tree โ Hierarchical structure, great for databases and file systems!
๐ 4. Matrix โ 2D representation, widely used in image processing and graphs!
๐ 5. Linked List โ A chain of nodes, efficient for insertions & deletions!
๐ 6. Graph โ Represents relationships, used in social networks & maps!
๐ 7. Heap (Max/Min) โ Optimized for priority-based operations!
๐ 8. Stack โ Last in, first out (LIFO). Undo/Redo in action!
๐ก 9. Trie โ Best for search & autocomplete functionalities!
๐ 10. HashMap & HashSet โ Fast lookups, perfect for key-value storage!
Understanding these will make you a better problem solver & efficient coder! ๐ก
๐ 1. Array โ Fixed-size collection of elements, perfect for fast lookups!
๐ฆ 2. Queue โ First in, first out (FIFO). Think of a line at a grocery store!
๐ณ 3. Tree โ Hierarchical structure, great for databases and file systems!
๐ 4. Matrix โ 2D representation, widely used in image processing and graphs!
๐ 5. Linked List โ A chain of nodes, efficient for insertions & deletions!
๐ 6. Graph โ Represents relationships, used in social networks & maps!
๐ 7. Heap (Max/Min) โ Optimized for priority-based operations!
๐ 8. Stack โ Last in, first out (LIFO). Undo/Redo in action!
๐ก 9. Trie โ Best for search & autocomplete functionalities!
๐ 10. HashMap & HashSet โ Fast lookups, perfect for key-value storage!
Understanding these will make you a better problem solver & efficient coder! ๐ก
Screenshot_13.png
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๐๐ฌ๐ข๐ง๐ ๐๐ข๐ -๐ ๐ข๐ง ๐๐ง๐ญ๐๐ซ๐ฏ๐ข๐๐ฐ๐ฌ ๐๐ง๐ ๐๐ฏ๐๐ซ๐ฒ๐๐๐ฒ ๐๐ข๐๐.
Big-O notation is a mathematical notation that is used to describe the performance or complexity of an algorithm, specifically how long an algorithm takes to run as the input size grows.
Understanding Big-O notation is essential for software engineers, as it allows them to analyze and compare the efficiency of different algorithms and make informed decisions about which one to use in a given situation.
Here are famous Big-O notations with examples.
Big-O notation is a mathematical notation that is used to describe the performance or complexity of an algorithm, specifically how long an algorithm takes to run as the input size grows.
Understanding Big-O notation is essential for software engineers, as it allows them to analyze and compare the efficiency of different algorithms and make informed decisions about which one to use in a given situation.
Here are famous Big-O notations with examples.
Database.png
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๐๐จ๐ฐ ๐ญ๐จ ๐ข๐ฆ๐ฉ๐ซ๐จ๐ฏ๐ ๐๐๐ญ๐๐๐๐ฌ๐ ๐ฉ๐๐ซ๐๐จ๐ซ๐ฆ๐๐ง๐๐?
Here are some of the top ways to improve database performance:
1. Indexing
Create the right indexes based on query patterns to speed up data retrieval.
2. Materialized Views
Store pre-computed query results for quick access, reducing the need to process complex queries repeatedly.
3. Vertical Scaling
Increase the capacity of the hashtag#database server by adding more CPU, RAM, or storage.
Here are some of the top ways to improve database performance:
1. Indexing
Create the right indexes based on query patterns to speed up data retrieval.
2. Materialized Views
Store pre-computed query results for quick access, reducing the need to process complex queries repeatedly.
3. Vertical Scaling
Increase the capacity of the hashtag#database server by adding more CPU, RAM, or storage.
API gateways.png
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๐๐จ๐ฉ ๐๐ข๐๐ซ๐จ๐ฌ๐๐ซ๐ฏ๐ข๐๐๐ฌ ๐๐๐ฌ๐ข๐ ๐ง ๐๐๐ญ๐ญ๐๐ซ๐ง๐ฌ
โก๏ธ 1. API Gateway Pattern: Centralizes external access to your microservices, simplifying communication and providing a single entry point for client requests.
โก๏ธ 2. Backends for Frontends Pattern (BFF): Creates dedicated backend services for each frontend, optimizing performance and user experience tailored to each platform.
โก๏ธ 3. Service Discovery Pattern: Enables microservices to dynamically discover and communicate with each other, simplifying service orchestration and enhancing system scalability.
โก๏ธ 4. Circuit Breaker Pattern: Implements a fault-tolerant mechanism for microservices, preventing cascading failures by automatically detecting and isolating faulty services.
โก๏ธ 5. Retry Pattern: Enhances microservices' resilience by automatically retrying failed operations, increasing the chances of successful execution and minimizing transient issues.
โก๏ธ 1. API Gateway Pattern: Centralizes external access to your microservices, simplifying communication and providing a single entry point for client requests.
โก๏ธ 2. Backends for Frontends Pattern (BFF): Creates dedicated backend services for each frontend, optimizing performance and user experience tailored to each platform.
โก๏ธ 3. Service Discovery Pattern: Enables microservices to dynamically discover and communicate with each other, simplifying service orchestration and enhancing system scalability.
โก๏ธ 4. Circuit Breaker Pattern: Implements a fault-tolerant mechanism for microservices, preventing cascading failures by automatically detecting and isolating faulty services.
โก๏ธ 5. Retry Pattern: Enhances microservices' resilience by automatically retrying failed operations, increasing the chances of successful execution and minimizing transient issues.