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Machine Learning with Python
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Forwarded from Data Analytics
π One of the most useful books on Agentic AI
This is not just a textbook, but a comprehensive overview of modern LLMs, model training, RL, inference, quality assessment, and building AI agents.
It's an excellent option to get a holistic picture and understand which topics deserve deeper study.
βοΈ Link to the book
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This is not just a textbook, but a comprehensive overview of modern LLMs, model training, RL, inference, quality assessment, and building AI agents.
It's an excellent option to get a holistic picture and understand which topics deserve deeper study.
βοΈ Link to the book
https://arxiv.org/abs/2606.24937
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Developers can also take dedicated courses on the Claude API, Claude Code, Model Context Protocol, Agent Skills, and cloud integrations.
All courses are free, and you don't need a Claude account to enroll. All materials and videos are in English.
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Anthropic has expanded its educational platform, Anthropic Academy. It now offers more than 20 free courses on using Claude and AI-powered tools.
No programming experience is required. There are programs for students, educators, nonprofits, and small businesses. The courses cover how to give models clear instructions, evaluate their responses, and automate everyday tasks.
Developers can also take dedicated courses on the Claude API, Claude Code, Model Context Protocol, Agent Skills, and cloud integrations.
All courses are free, and you don't need a Claude account to enroll. All materials and videos are in English.
π Participants receive official certificates after completing many of the programs.
β‘οΈ Find all the courses here.
https://anthropic.com/learn/courses
Planning to take one?
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My new hobby: searching for beautiful projects on GitHub and being inspired by their design. πβ¨
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Towards Data Science
How Far Can Classical NLP Go? From Bag-of-Words to Stacking on Spooky Author Identification | Towards Data Science
An end-to-end classical NLP experiment on Kaggleβs Spooky Author Identification task: from Vowpal Wabbit and TF-IDF/NB-SVM baselines to a tuned stacked ensemble, with a compact representation survey of Bag-of-Words, BM25, Word2Vec, and FastText for context.
π Looking for a portfolio-ready NLP project?
I recently published an end-to-end walkthrough on Towards Data Science using Kaggleβs Spooky Author Identification dataset.
Youβll see how far classical NLP can go with:
π Bag-of-Words and TF-IDF
π€ Character n-grams
π Model comparison
π§© Ensemble stacking
Itβs a practical project for anyone preparing for an ML/DS role, with no deep learning required. I walk through the entire workflow step by step:
π https://towardsdatascience.com/how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification/
I recently published an end-to-end walkthrough on Towards Data Science using Kaggleβs Spooky Author Identification dataset.
Youβll see how far classical NLP can go with:
π Bag-of-Words and TF-IDF
π€ Character n-grams
π Model comparison
π§© Ensemble stacking
Itβs a practical project for anyone preparing for an ML/DS role, with no deep learning required. I walk through the entire workflow step by step:
π https://towardsdatascience.com/how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification/
β€7π1