qu4nt

Ofrecer soluciones guiadas por datos es un reto. Buscamos el uso inteligente de información para la acción colectiva. #qu4nt #datascience #datadriven

[qu4nt @_qu4nt]

Puedes consultar nuestra traducción al español 💻 de la documentación de Sci-kit learn 0.24.2 ahora disponible en:

➡https://t.co/WYyTHLRDm7
#qu4nt #announces #Sklearn

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Important Sklearn Methods For Machine Learning
v/@PythonPr

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Stanford HAI Launches AI and Organizations Lab to Study Science of AI in the Workplace
https://t.co/MG0yeQjdND

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How AI is Transforming Scientific Discovery While Keeping Humans at the Center
https://t.co/daizEzb6xh

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Cisco rolls out software tools to protect IT systems from AI agents
https://t.co/pnEb974AXd

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NBA plans AI system for automatic out-of-bounds calls
https://t.co/pS22raywUb

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Anthropic releases Claude Opus 4.8
https://t.co/xOINAE4mWr

[Python Programming @PythonPr]

The World of Python Developers

[Python Programming @PythonPr]

For some reason, nobody uses R for machine learning

[Arun Chauhan @imarun_chauhan]

MASTER SYSTEM DESIGN

SYSTEM DESIGN MASTER TREE
│
├── 1. Fundamentals
│ ├── What is System Design
│ ├── Functional Requirements
│ ├── Non-Functional Requirements
│ │ ├── Scalability
│ │ ├── Reliability
│ │ ├── Availability
│ │ ├── Consistency
│ │ └── Performance
│ └── Trade-offs
│
├── 2. Architecture Basics
│ ├── Monolithic Architecture
│ ├── Microservices Architecture
│ ├── Service-Oriented Architecture (SOA)
│ ├── Layered Architecture
│ └── Event-Driven Architecture
│
├── 3. Networking Basics
│ ├── HTTP / HTTPS
│ ├── TCP / IP
│ ├── DNS
│ ├── Load Balancing Concepts
│ └── Latency vs Throughput
│
├── 4. Load Balancing
│ ├── Types (L4 / L7)
│ ├── Algorithms
│ │ ├── Round Robin
│ │ ├── Least Connections
│ │ └── IP Hash
│ ├── Reverse Proxy
│ └── Failover Strategies
│
├── 5. Databases
│ ├── SQL vs NoSQL
│ ├── Relational Databases
│ ├── NoSQL Types
│ │ ├── Key-Value
│ │ ├── Document
│ │ ├── Column
│ │ └── Graph
│ ├── CAP Theorem
│ └── ACID vs BASE
│
├── 6. Caching
│ ├── Why Caching
│ ├── Cache Layers
│ │ ├── Client-side
│ │ ├── CDN
│ │ ├── Server-side
│ ├── Cache Strategies
│ │ ├── Write-through
│ │ ├── Write-back
│ │ └── Cache-aside
│ └── Cache Invalidation
│
├── 7. Data Partitioning
│ ├── Vertical Partitioning
│ ├── Horizontal Partitioning (Sharding)
│ ├── Consistent Hashing
│ └── Rebalancing
│
├── 8. Replication
│ ├── Master-Slave
│ ├── Master-Master
│ ├── Synchronous Replication
│ └── Asynchronous Replication
│
├── 9. Consistency Models
│ ├── Strong Consistency
│ ├── Eventual Consistency
│ ├── Causal Consistency
│ └── Read-after-Write Consistency
│
├── 10. Messaging Systems
│ ├── Message Queues
│ ├── Pub/Sub Systems
│ ├── Kafka / RabbitMQ
│ ├── Event Streaming
│ └── Exactly-once / At-least-once Delivery
│
├── 11. API Design
│ ├── REST
│ ├── GraphQL
│ ├── gRPC
│ ├── Rate Limiting
│ └── API Gateway
│
├── 12. Storage Systems
│ ├── File Storage
│ ├── Object Storage (S3)
│ ├── Block Storage
│ └── CDN (Content Delivery Network)
│
├── 13. Security
│ ├── Authentication
│ ├── Authorization
│ ├── Encryption (SSL/TLS)
│ ├── Firewalls
│ └── DDoS Protection
│
├── 14. Scalability
│ ├── Vertical Scaling
│ ├── Horizontal Scaling
│ ├── Auto Scaling
│ └── Stateless Services
│
├── 15. Fault Tolerance
│ ├── Redundancy
│ ├── Circuit Breaker
│ ├── Retries & Backoff
│ └── Graceful Degradation
│
├── 16. Observability
│ ├── Logging
│ ├── Monitoring
│ ├── Metrics
│ ├── Alerting
│ └── Distributed Tracing
│
├── 17. Deployment & DevOps
│ ├── CI/CD
│ ├── Docker
│ ├── Kubernetes
│ ├── Blue-Green Deployment
│ └── Canary Releases
│
└── 18. Real-World System Design
├── URL Shortener (like Bitly)
├── Chat System (like WhatsApp)
├── Video Streaming (like YouTube)
├── Ride Sharing (like Uber)
└── Social Media Feed (like Twitter/X)

@BharukaShraddha
Thanks for sharing, great work 👏

[Python Programming @PythonPr]

Machine Learning Time Complexity

[Roxo @alexhalescoder]

How to Master Claude in One Week
Follow me for more @alexhalescoder

[qu4nt @_qu4nt]

Best Platforms For AI Agents
v/@sonalshukla3377

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v/@ComputerrBooks

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Learn Claude Code
v/@tut_ml
https://t.co/2lupVF2Hfl