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π₯Whether you're preparing for #Python, #Cisco, #PMI, #Fortinet, #AWS, #Azure, #AI, #Excel, #comptia, #ITIL, #cloud or any other in-demand certification β SPOTO has got you covered!
β Whatβs Inside:
γ»Free Python, Excel, Cyber Security, Cisco, SQL, ITIL, PMP, AWS courses: https://bit.ly/3M9h5su
γ»IT Certs E-book: https://bit.ly/3Mlu5ez
γ»IT Exams Skill Test: https://bit.ly/3NVrgRU
γ»Free Cloud Study Guide: https://bit.ly/4kgFVDs
γ»Free AI material and support toolsοΌhttps://bit.ly/46qvpDX
π Become Part of Our IT Learning Circle! resources and support:
https://chat.whatsapp.com/FlG2rOYVySLEHLKXF3nKGB
π¬ Want exam help? Chat with an admin now!
wa.link/8fy3x4
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If you want to understand AI not through "vacuum" courses, but through real open-source projects - here's a top list of repos that really lead you from the basics to practice:
1) Karpathy β Neural Networks: Zero to Hero
The most understandable introduction to neural networks and backprop "in layman's terms"
https://github.com/karpathy/nn-zero-to-hero
2) Hugging Face Transformers
The main library of modern NLP/LLM: models, tokenizers, fine-tuning
https://github.com/huggingface/transformers
3) FastAI β Fastbook
Practical DL training through projects and experiments
https://github.com/fastai/fastbook
4) Made With ML
ML as an engineering system: pipelines, production, deployment, monitoring
https://github.com/GokuMohandas/Made-With-ML
5) Machine Learning System Design (Chip Huyen)
How to build ML systems in real business: data, metrics, infrastructure
https://github.com/chiphuyen/machine-learning-systems-design
6) Awesome Generative AI Guide
A collection of materials on GenAI: from basics to practice
https://github.com/aishwaryanr/awesome-generative-ai-guide
7) Dive into Deep Learning (D2L)
One of the best books on DL + code + assignments
https://github.com/d2l-ai/d2l-en
Save it for yourself - this is a base on which you can really grow into an ML/LLM engineer.
#Python #datascience #DataAnalysis #MachineLearning #AI #DeepLearning #LLMS
https://t.iss.one/CodeProgrammer
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π Generative AI Training for Beginners
A course from Microsoft with 21 lessons covering the basics of creating applications based on generative AI. Each lesson includes theory and practical examples in Python and TypeScript, allowing you to learn at a comfortable pace.
π Key features:
- 21 lessons on generative #AI
- Support for Python and TypeScript
- Lessons with theory and practical tasks
- Additional resources for in-depth study
- Multilingual support
π GitHub: https://github.com/microsoft/generative-ai-for-beginners
#python #LLMS #generative_Ai
https://t.iss.one/CodeProgrammer
A course from Microsoft with 21 lessons covering the basics of creating applications based on generative AI. Each lesson includes theory and practical examples in Python and TypeScript, allowing you to learn at a comfortable pace.
π Key features:
- 21 lessons on generative #AI
- Support for Python and TypeScript
- Lessons with theory and practical tasks
- Additional resources for in-depth study
- Multilingual support
π GitHub: https://github.com/microsoft/generative-ai-for-beginners
#python #LLMS #generative_Ai
https://t.iss.one/CodeProgrammer
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Pandas vs. Polars: A Complete Comparison of Syntax, Speed, and Memory
Need help choosing the right #Python dataframe library? This article compares #Pandas and #Polars to help you decide.
If you've been working with data in Python, you've almost certainly used pandas. It's been the go-to library for data manipulation for over a decade. But recently, Polars has been gaining serious traction. Polars promises to be faster, more memory-efficient, and more intuitive than pandas. But is it worth learning? And how different is it really?
In this article, we'll compare pandas and Polars side-by-side. You'll see performance benchmarks, and learn the syntax differences. By the end, you'll be able to make an informed decision for your next data project.
Read: https://www.kdnuggets.com/pandas-vs-polars-a-complete-comparison-of-syntax-speed-and-memory
https://t.iss.one/CodeProgrammer πΊ
Need help choosing the right #Python dataframe library? This article compares #Pandas and #Polars to help you decide.
If you've been working with data in Python, you've almost certainly used pandas. It's been the go-to library for data manipulation for over a decade. But recently, Polars has been gaining serious traction. Polars promises to be faster, more memory-efficient, and more intuitive than pandas. But is it worth learning? And how different is it really?
In this article, we'll compare pandas and Polars side-by-side. You'll see performance benchmarks, and learn the syntax differences. By the end, you'll be able to make an informed decision for your next data project.
Read: https://www.kdnuggets.com/pandas-vs-polars-a-complete-comparison-of-syntax-speed-and-memory
https://t.iss.one/CodeProgrammer πΊ
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