Forwarded from Machine Learning with Python
π§ Python libraries for AI agents - complexity of learning π₯
π’ Easy
β’ LangChain
β’ tool calling
β’ agent memory
β’ simple agents
β’ CrewAI
β’ agents with roles
β’ collaboration of several agents
β’ SmolAgents
β’ lightweight agents
β’ quick experiments
π‘ Medium
β’ LangGraph
β’ stateful workflow
β’ agent orchestration
β’ LlamaIndex
β’ RAG pipelines
β’ data indexing
β’ knowledge agents
β’ OpenAI Agents SDK
β’ tool integrations
β’ agent workflows
β’ Strands
β’ agent orchestration
β’ task coordination
β’ Semantic Kernel
β’ skills / plugins
β’ AI process orchestration
β’ PydanticAI
β’ typed LLM applications
β’ structured agent workflows
β’ Langroid
β’ message exchange between agents
β’ interaction with tools
π΄ Difficult
β’ AutoGen
β’ multi-agent dialogues
β’ autonomous agent cooperation
β’ DSPy
β’ programmable prompting
β’ optimization of LLM pipelines
β’ A2A
β’ agent-to-agent protocol
β’ distributed agent systems
https://t.me/CodeProgrammerβ
π’ Easy
β’ LangChain
β’ tool calling
β’ agent memory
β’ simple agents
β’ CrewAI
β’ agents with roles
β’ collaboration of several agents
β’ SmolAgents
β’ lightweight agents
β’ quick experiments
π‘ Medium
β’ LangGraph
β’ stateful workflow
β’ agent orchestration
β’ LlamaIndex
β’ RAG pipelines
β’ data indexing
β’ knowledge agents
β’ OpenAI Agents SDK
β’ tool integrations
β’ agent workflows
β’ Strands
β’ agent orchestration
β’ task coordination
β’ Semantic Kernel
β’ skills / plugins
β’ AI process orchestration
β’ PydanticAI
β’ typed LLM applications
β’ structured agent workflows
β’ Langroid
β’ message exchange between agents
β’ interaction with tools
π΄ Difficult
β’ AutoGen
β’ multi-agent dialogues
β’ autonomous agent cooperation
β’ DSPy
β’ programmable prompting
β’ optimization of LLM pipelines
β’ A2A
β’ agent-to-agent protocol
β’ distributed agent systems
https://t.me/CodeProgrammer
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Forwarded from Code With Python
This channels is for Programmers, Coders, Software Engineers.
0οΈβ£ Python
1οΈβ£ Data Science
2οΈβ£ Machine Learning
3οΈβ£ Data Visualization
4οΈβ£ Artificial Intelligence
5οΈβ£ Data Analysis
6οΈβ£ Statistics
7οΈβ£ Deep Learning
8οΈβ£ programming Languages
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https://t.me/Codeprogrammer
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π I Stole a Wall Street Trick to Solve a Google Trends Data Problem
π Category: DATA SCIENCE
π Date: 2026-03-09 | β±οΈ Read time: 14 min read
A methodology for comparing Google Trends data across countries.
#DataScience #AI #Python
π Category: DATA SCIENCE
π Date: 2026-03-09 | β±οΈ Read time: 14 min read
A methodology for comparing Google Trends data across countries.
#DataScience #AI #Python
π Building a Like-for-Like solution for Stores in Power BI
π Category: DATA ANALYSIS
π Date: 2026-03-10 | β±οΈ Read time: 10 min read
Like-for-Like (L4L) solutions are essential for comparing elements. Itβs about comparing only comparable elements, inβ¦
#DataScience #AI #Python
π Category: DATA ANALYSIS
π Date: 2026-03-10 | β±οΈ Read time: 10 min read
Like-for-Like (L4L) solutions are essential for comparing elements. Itβs about comparing only comparable elements, inβ¦
#DataScience #AI #Python
π What Are Agent Skills Beyond Claude?
π Category: AGENTIC AI
π Date: 2026-03-10 | β±οΈ Read time: 6 min read
How to design and implement agent skills for custom agents outside the Claude ecosystem
#DataScience #AI #Python
π Category: AGENTIC AI
π Date: 2026-03-10 | β±οΈ Read time: 6 min read
How to design and implement agent skills for custom agents outside the Claude ecosystem
#DataScience #AI #Python
Forwarded from Machine Learning with Python
π A fresh deep learning course from MIT is now publicly available
A full-fledged educational course has been published on the university's website: 24 lectures, practical assignments, homework, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
β‘οΈ Link to the course
tags: #Python #DataScience #DeepLearning #AI
A full-fledged educational course has been published on the university's website: 24 lectures, practical assignments, homework, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
β‘οΈ Link to the course
tags: #Python #DataScience #DeepLearning #AI
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π Hybrid Neuro-Symbolic Fraud Detection: Guiding Neural Networks with Domain Rules
π Category: DEEP LEARNING
π Date: 2026-03-10 | β±οΈ Read time: 14 min read
I really thought I was onto something big: add a couple of simple domain rulesβ¦
#DataScience #AI #Python
π Category: DEEP LEARNING
π Date: 2026-03-10 | β±οΈ Read time: 14 min read
I really thought I was onto something big: add a couple of simple domain rulesβ¦
#DataScience #AI #Python
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π When Data Lies: Finding Optimal Strategies for Penalty Kicks with Game Theory
π Category: DATA SCIENCE
π Date: 2026-03-10 | β±οΈ Read time: 9 min read
A data-driven introduction to game theory, Nash equilibrium, and strategic decision-making
#DataScience #AI #Python
π Category: DATA SCIENCE
π Date: 2026-03-10 | β±οΈ Read time: 9 min read
A data-driven introduction to game theory, Nash equilibrium, and strategic decision-making
#DataScience #AI #Python
Forwarded from Machine Learning with Python
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Enter the Draw π: https://bit.ly/3NwkceD
π Become Part of Our IT Learning Circle! resources and support:
https://chat.whatsapp.com/Cnc5M5353oSBo3savBl397
π¬ Want exam help? Chat with an admin now!
wa.link/rozuuw
β°Last Chance β Get It Before Itβs Gone!
π How the Fourier Transform Converts Sound Into Frequencies
π Category: MACHINE LEARNING
π Date: 2026-03-11 | β±οΈ Read time: 26 min read
A visual, intuition-first guide to understanding what the math is really doing β from windingβ¦
#DataScience #AI #Python
π Category: MACHINE LEARNING
π Date: 2026-03-11 | β±οΈ Read time: 26 min read
A visual, intuition-first guide to understanding what the math is really doing β from windingβ¦
#DataScience #AI #Python
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π An Intuitive Guide to MCMC (Part I): The Metropolis-Hastings Algorithm
π Category: MATH
π Date: 2026-03-11 | β±οΈ Read time: 14 min read
Tired of the AI hype? Letβs talk about the probabilistic algorithms actually driving high-end quantitativeβ¦
#DataScience #AI #Python
π Category: MATH
π Date: 2026-03-11 | β±οΈ Read time: 14 min read
Tired of the AI hype? Letβs talk about the probabilistic algorithms actually driving high-end quantitativeβ¦
#DataScience #AI #Python
π Spectral Clustering Explained: How Eigenvectors Reveal Complex Cluster Structures
π Category: MACHINE LEARNING
π Date: 2026-03-11 | β±οΈ Read time: 10 min read
Understanding why spectral clustering outperforms K-means
#DataScience #AI #Python
π Category: MACHINE LEARNING
π Date: 2026-03-11 | β±οΈ Read time: 10 min read
Understanding why spectral clustering outperforms K-means
#DataScience #AI #Python
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π Why Most A/B Tests Are Lying to You
π Category: DATA SCIENCE
π Date: 2026-03-11 | β±οΈ Read time: 14 min read
The 4 statistical sins that invalidate most A/B tests, plus a pre-test checklist and Bayesianβ¦
#DataScience #AI #Python
π Category: DATA SCIENCE
π Date: 2026-03-11 | β±οΈ Read time: 14 min read
The 4 statistical sins that invalidate most A/B tests, plus a pre-test checklist and Bayesianβ¦
#DataScience #AI #Python
π Exploratory Data Analysis for Credit Scoring with Python
π Category: DATA SCIENCE
π Date: 2026-03-12 | β±οΈ Read time: 16 min read
Understanding default risk through statistical analysis of borrower and loan characteristics.
#DataScience #AI #Python
π Category: DATA SCIENCE
π Date: 2026-03-12 | β±οΈ Read time: 16 min read
Understanding default risk through statistical analysis of borrower and loan characteristics.
#DataScience #AI #Python
Forwarded from Machine Learning with Python
Machine Learning in python.pdf
1 MB
Machine Learning in Python (Course Notes)
I just went through an amazing resource on #MachineLearning in #Python by 365 Data Science, and I had to share the key takeaways with you!
Hereβs what youβll learn:
π Linear Regression - The foundation of predictive modeling
π Logistic Regression - Predicting probabilities and classifications
π Clustering (K-Means, Hierarchical) - Making sense of unstructured data
π Overfitting vs. Underfitting - The balancing act every ML engineer must master
π OLS, R-squared, F-test - Key metrics to evaluate your models
https://t.me/CodeProgrammer || Shareπ and Like π
I just went through an amazing resource on #MachineLearning in #Python by 365 Data Science, and I had to share the key takeaways with you!
Hereβs what youβll learn:
π Linear Regression - The foundation of predictive modeling
π Logistic Regression - Predicting probabilities and classifications
π Clustering (K-Means, Hierarchical) - Making sense of unstructured data
π Overfitting vs. Underfitting - The balancing act every ML engineer must master
π OLS, R-squared, F-test - Key metrics to evaluate your models
https://t.me/CodeProgrammer || Share
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Forwarded from Machine Learning with Python
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π Solving the Human Training Data Problem
π Category: LARGE LANGUAGE MODELS
π Date: 2026-03-12 | β±οΈ Read time: 18 min read
How AI has completely transformed the way I study as a graduate student
#DataScience #AI #Python
π Category: LARGE LANGUAGE MODELS
π Date: 2026-03-12 | β±οΈ Read time: 18 min read
How AI has completely transformed the way I study as a graduate student
#DataScience #AI #Python
π Scaling Vector Search: Comparing Quantization and Matryoshka Embeddings for 80% Cost Reduction
π Category: MACHINE LEARNING
π Date: 2026-03-12 | β±οΈ Read time: 11 min read
Navigating the performance cliff: How pairing MRL with int8 and binary quantization balances infrastructure costsβ¦
#DataScience #AI #Python
π Category: MACHINE LEARNING
π Date: 2026-03-12 | β±οΈ Read time: 11 min read
Navigating the performance cliff: How pairing MRL with int8 and binary quantization balances infrastructure costsβ¦
#DataScience #AI #Python
π I Finally Built My First AI App (And It Wasnβt What I Expected)
π Category: LARGE LANGUAGE MODELS
π Date: 2026-03-12 | β±οΈ Read time: 14 min read
A beginner-friendly walkthrough of API calls, environment variables, and real-world AI infrastructure
#DataScience #AI #Python
π Category: LARGE LANGUAGE MODELS
π Date: 2026-03-12 | β±οΈ Read time: 14 min read
A beginner-friendly walkthrough of API calls, environment variables, and real-world AI infrastructure
#DataScience #AI #Python
β€1