Machine Learning with Python
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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.

Admin: @HusseinSheikho || @Hussein_Sheikho
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๐ŸŽ“ Thesis โ€ข Dissertation โ€ข Research โ€ข Programming โ€ข Simulation

From a single research ideaโ€ฆ
to a complete academic masterpiece.

๐Ÿ”น Professional assistance for:
โœ”๏ธ Masterโ€™s & PhD Theses
โœ”๏ธ ISI / Scopus Articles
โœ”๏ธ Research Proposals & Methodology
โœ”๏ธ Data Analysis & Statistical Modeling
โœ”๏ธ AI & Machine Learning Projects
โœ”๏ธ MATLAB โ€ข Python โ€ข Simulink โ€ข Abaqus โ€ข COMSOL โ€ข Ansys โ€ข ETAP โ€ข PSCAD โ€ข HOMER โ€ข Proteus โ€ข LabVIEW
โœ”๏ธ Electrical, Civil, Mechanical, Medical, Management, Computer Science & All Engineering Fields
โœ”๏ธ Rare & High-Quality Datasets
โœ”๏ธ Simulation Projects & Optimization Algorithms
โœ”๏ธ Academic Presentation Design
โœ”๏ธ Journal Revision & Reviewer Response Preparation

๐Ÿ“Š Accurate Results
๐Ÿ“š Professional Documentation
๐Ÿ’ป Clean & Structured Coding
๐Ÿ”’ Full Confidentiality
โณ On-Time Delivery

Your research deserves more than copy-paste work.
It deserves precision, originality, and engineering-level thinking.

โœจ Turning complex ideas into publishable research.

๐Ÿ“ฉ Contact us for consultation and project evaluation.
https://t.me/Omidyzd62
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๐Ÿ“š AI / ML / DL Learning Resources Hub ๐Ÿค–๐Ÿง 

A structured, end-to-end roadmap to master AI โ€” from fundamentals to cutting-edge research. ๐Ÿš€

A carefully curated, all-in-one repository designed to help Computer Science students, AI enthusiasts, and professionals ๐Ÿ‘ฉโ€๐Ÿ’ป๐Ÿ‘จโ€๐Ÿ’ป who want to build strong foundations and progress confidently from beginner to advanced levels ๐Ÿ“ˆ. This hub brings together the high-quality books ๐Ÿ“–, courses ๐ŸŽ“, playlists ๐ŸŽต, research papers ๐Ÿ“, tools ๐Ÿ› , and learning roadmaps ๐Ÿ—บ covering: Artificial Intelligence, Machine Learning, Deep Learning, Data Science ๐Ÿ“Š, Transformers, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and MLOps ๐Ÿ”„, all organized in a clear, practical, and industry-relevant manner.

The resources are selected to balance theory ๐Ÿง , intuition ๐Ÿ’ก, and real-world application ๐ŸŒ, allowing learners to follow modules sequentially or in parallel โณ based on their goals.

โญ๏ธ Recommended resources highlight high-impact content widely used in academia ๐Ÿ›, research ๐Ÿ”ฌ, and industry ๐Ÿญ, ensuring you focus on what truly matters in modern AI.

๐Ÿ†˜ Repo: https://github.com/bishwaghimire/ai-learning-roadmaps

By: https://t.me/CodeProgrammer
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๐ŸŽ“ Thesis โ€ข Dissertation โ€ข Research โ€ข Programming โ€ข Simulation

From a single research ideaโ€ฆ
to a complete academic masterpiece.

๐Ÿ”น Professional assistance for:
โœ”๏ธ Masterโ€™s & PhD Theses
โœ”๏ธ ISI / Scopus Articles
โœ”๏ธ Research Proposals & Methodology
โœ”๏ธ Data Analysis & Statistical Modeling
โœ”๏ธ AI & Machine Learning Projects
โœ”๏ธ MATLAB โ€ข Python โ€ข Simulink โ€ข Abaqus โ€ข COMSOL โ€ข Ansys โ€ข ETAP โ€ข PSCAD โ€ข HOMER โ€ข Proteus โ€ข LabVIEW
โœ”๏ธ Electrical, Civil, Mechanical, Medical, Management, Computer Science & All Engineering Fields
โœ”๏ธ Rare & High-Quality Datasets
โœ”๏ธ Simulation Projects & Optimization Algorithms
โœ”๏ธ Academic Presentation Design
โœ”๏ธ Journal Revision & Reviewer Response Preparation

๐Ÿ“Š Accurate Results
๐Ÿ“š Professional Documentation
๐Ÿ’ป Clean & Structured Coding
๐Ÿ”’ Full Confidentiality
โณ On-Time Delivery

Your research deserves more than copy-paste work.
It deserves precision, originality, and engineering-level thinking.

โœจ Turning complex ideas into publishable research.

๐Ÿ“ฉ Contact us for consultation and project evaluation.
https://t.me/Omidyzd62
โค7๐Ÿ‘2
Automate research with NotebookLM + Python ๐Ÿค–๐Ÿ

Notebooklm-py โ€” is a unofficial library for working with Google NotebookLM,๐Ÿ“š๐Ÿง  which allows automating research tasks, generating content, and connecting AI agents. Suitable for prototypes, pet projects, and personal tools โ€” works both via Python and CLI โŒจ๏ธ

What it can do:

โ€ข integration with AI agents and Claude Code ๐Ÿค–
โ€ข automatic import and processing of sources ๐Ÿ“ฅ
โ€ข generation of podcasts, videos, and educational materials ๐ŸŽ™๏ธ๐ŸŽฅ๐Ÿ“–
โ€ข working via Python API and command line ๐Ÿ’ป
โ€ข using unofficial Google APIs ๐Ÿ”ง
https://github.com/teng-lin/notebooklm-py

https://t.me/CodeProgrammer ๐Ÿ“ฑ
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Forwarded from Machine Learning
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Google Gemma 4's pre-training is completely free

All you need is a browser and access to more than 500 models to choose from.

The process is simple:

1. Open the notebook of Unsloth in Colab
2. Select a model and a dataset
3. Start the trainin

Link: https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb

It's done ๐Ÿ˜‚

๐Ÿ‘‰ https://t.me/MachineLearning9
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14 minutes with an Anthropic engineer will teach you more about building agents ๐Ÿค– than most devs figure out in months of trial and error ๐Ÿ› .

Same guy who wrote โ€œBuilding Effective Agentsโ€, the post every AI builder has bookmarked ๐Ÿ“‘.

No fluff. No 47-tool frameworks. Just the patterns that actually work in production ๐Ÿš€:

โ†’ When to use workflows vs. agents (most people get this wrong) โŒ
โ†’ Why simple > clever, every single time โœ…
โ†’ The orchestrator-worker pattern that scales ๐Ÿ“ˆ
โ†’ When NOT to build an agent at all ๐Ÿ›‘

If youโ€™re shipping AI products in 2026 and havenโ€™t watched this, youโ€™re doing it on hard mode ๐ŸŽฎ.

14 minutes. Bookmark it ๐Ÿ“Œ. Watch it twice ๐Ÿ‘€.

#AI #Agents #Tech #DevCommunity #FutureTech #ProgrammingConcepts
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๐Ÿ”– Interactive textbook on probability theory and statistics ๐Ÿ“Šโœจ

A super-intuitive site where you can visually study distributions, sampling, and statistical concepts. ๐Ÿ“ˆ๐ŸŽฒ

No tons of formulas and boring theory โ€” everything is demonstrated through interactive examples and simulations. ๐Ÿ’ป๐Ÿ”ฌ

โ›“๏ธ Download here ๐Ÿ‘‡
https://seeing-theory.brown.edu/

#Probability #Statistics #DataScience #Learning #Interactive #Math

https://t.me/CodeProgrammer
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Most people donโ€™t fail because they lack ambition ๐ŸŽฏ

They fail because their time and energy leak into distractions before they ever reach the goal ๐Ÿ•ฐ๐Ÿ’”

This visual explains it perfectly: ๐Ÿ“Š

Time + Energy are only useful when filtered through discipline ๐Ÿง ๐Ÿ›ก

Without discipline, distractions absorb everything: ๐ŸŒŠ

โ€ข endless notifications ๐Ÿ“ฑ
โ€ข reactive meetings ๐Ÿค
โ€ข poor sleep ๐Ÿ˜ด
โ€ข stress-driven habits ๐Ÿคฏ
โ€ข multitasking disguised as productivity ๐Ÿ”„

And in high-performance environments, this becomes a leadership issue, not just a personal one ๐Ÿข๐Ÿ“‰

I see this often in corporate wellness workshops and executive coaching sessions. ๐Ÿ—ฃ

Leaders want better focus, resilience, and performance โšก๏ธ
But the real challenge is not motivation ๐Ÿšซ๐Ÿ”ฅ

Itโ€™s protecting their cognitive energy daily ๐Ÿง ๐Ÿ”’

Discipline is not punishment โ›”๏ธ
Itโ€™s a system that helps you direct your energy intentionally ๐ŸŽฏ

Sometimes that means: โœ”๏ธ

โœ”๏ธ starting the day without your phone ๐Ÿ“ต
โœ”๏ธ eating to stabilize energy and focus ๐Ÿฅ—
โœ”๏ธ creating recovery moments between meetings โธ๏ธ
โœ”๏ธ reducing unnecessary inputs ๐Ÿ“‰
โœ”๏ธ building routines that lower decision fatigue ๐Ÿง˜

Because peak performance is rarely about doing more ๐Ÿƒ
Itโ€™s about allowing less distraction to consume what matters most ๐ŸŽฏโœจ

Your goals are not only built by effort ๐Ÿ’ช
They are built by what you consistently refuse to give your energy to ๐Ÿšซ๐Ÿ•ธ

And if youโ€™re ready to start, I created something simple for you: ๐Ÿš€

My 7 Days to Peak Performance email series ๐Ÿ“ง
designed to help you improve your energy, focus, and productivity with practical daily strategies you can actually stick to. ๐Ÿ“…โœ…

You can join here: ๐Ÿ”—

https://lnkd.in/eA3h9wb8

#PeakPerformance #ProductivityHacks #FocusMastery #Discipline #ExecutiveCoaching #MindsetShift
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๐Ÿ™๐Ÿ’ธ 500$ FOR THE FIRST 500 WHO JOIN THE CHANNEL! ๐Ÿ™๐Ÿ’ธ

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You can join at this link! ๐Ÿ‘†๐Ÿ‘‡

https://t.me/+-WZeIeP8YI8wM2E6
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๐Ÿš€ Demystifying Activation Functions! ๐Ÿง โœจ

Ever wondered why activation functions are so critical in neural networks? ๐Ÿค”๐Ÿค–

Theyโ€™re the secret sauce that allows models to capture complex, nonlinear relationships! ๐Ÿ”ฅ๐Ÿ“ˆ

Do you want to learn how to implement an artificial neural network from scratch in Python using NumPy? ๐Ÿ๐Ÿ“Š

Learn more in super-detailed guide: https://lnkd.in/e4CydTtB ๐Ÿ”—๐Ÿ“š

#NeuralNetworks #DeepLearning #ActivationFunctions #Python #NumPy #AI
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