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Donโt miss your chance to level up your networking career in 2026! ๐
Want to become a certified Network Engineer and boost your IT career in 2026? ๐
Whether you're preparing for #CCNA #CCNP or even #CCIE, this is your chance to get premium Cisco learning resources & insider study support!
๐ฅ What Youโll Get:
๐ Cisco Training Roadmaps
๐ Networking Lab Guides
๐ Command Cheat Sheets
๐ Cisco Official eBooks
๐ Real Practice Questions
๐ Exam Preparation Tips
๐ FREE Starter Resources Available:
๐โ CCNA Beginner Notes:https://reurl.cc/j6bqey
๐โ CCNP Study Checklist:https://reurl.cc/npWnbn
๐ฉ To receive all FREE Cisco materials directly in your inbox:
wa.link/8i0msc
๐ Connect us and Leave your email to get instant access!
๐ก Bonus for subscribers:
https://chat.whatsapp.com/FLth69u2WswIlZ6bta2SJf
โ Exclusive study group invitations
โ Latest Cisco exam changes
โ Fast-track learning strategies
โ Priority access to upcoming training sessions
โก Thousands of IT learners are already preparing smarter.
Donโt miss your chance to level up your networking career in 2026! ๐
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Don't learn ML by randomly jumping through tutorials. ๐ซ๐
DS-ML Bootcamp is a public repository for a Data Science and machine learning course for beginners who want a structured path from zero to practical projects. ๐๐
It helps transition from installation and concepts to practical ML work, organizing lessons, assignments, code examples, datasets, and solutions around the main machine learning workflow. ๐ ๏ธ๐ง
Key features:
- End-to-end workflow - covers data collection, preprocessing, train/test split, model selection, training, evaluation, and deployment ๐๐
- Lesson-based structure - starts with tools/setup, Data Science, ML, data fundamentals, and regression ๐๐งฎ
- Practical materials - assignments give learners structured tasks, not just reading notes โ๏ธโ
- Code + datasets - Python examples and raw CSV datasets included for exercises ๐๐
- Set up for repetition - the README says you can clone the repository and use Jupyter or VS Code while going through lessons ๐ป๐
Free public repository on GitHub. ๐
https://github.com/goobolabs/ds-ml-bootcamp
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DS-ML Bootcamp is a public repository for a Data Science and machine learning course for beginners who want a structured path from zero to practical projects. ๐๐
It helps transition from installation and concepts to practical ML work, organizing lessons, assignments, code examples, datasets, and solutions around the main machine learning workflow. ๐ ๏ธ๐ง
Key features:
- End-to-end workflow - covers data collection, preprocessing, train/test split, model selection, training, evaluation, and deployment ๐๐
- Lesson-based structure - starts with tools/setup, Data Science, ML, data fundamentals, and regression ๐๐งฎ
- Practical materials - assignments give learners structured tasks, not just reading notes โ๏ธโ
- Code + datasets - Python examples and raw CSV datasets included for exercises ๐๐
- Set up for repetition - the README says you can clone the repository and use Jupyter or VS Code while going through lessons ๐ป๐
Free public repository on GitHub. ๐
https://github.com/goobolabs/ds-ml-bootcamp
#MachineLearning #DataScience #Coding #Python #AI #Learning
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GitHub
GitHub - goobolabs/ds-ml-bootcamp: Data Science and Machine Learning Bootcamp. (Jun - 2026)
Data Science and Machine Learning Bootcamp. (Jun - 2026) - goobolabs/ds-ml-bootcamp
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The math.perm() method
The math.perm() method in Python returns the number of ways to select k elements from n elements, with and without repetition. ๐งฎ
Syntax:
Where:
n: The number of elements from which k elements are selected.
k: The number of elements that are selected.
In the first example, the method returns the number of ways to select 3 elements from 5 elements. The result is 60 ways. ๐
In the second example, the method returns the number of ways to select 5 elements from 10 elements. The result is 252 ways. ๐
#Python #Math #Coding #Programming #DataScience #Tech
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The math.perm() method in Python returns the number of ways to select k elements from n elements, with and without repetition. ๐งฎ
Syntax:
math.perm(n, k)
Where:
n: The number of elements from which k elements are selected.
k: The number of elements that are selected.
In the first example, the method returns the number of ways to select 3 elements from 5 elements. The result is 60 ways. ๐
In the second example, the method returns the number of ways to select 5 elements from 10 elements. The result is 252 ways. ๐
#Python #Math #Coding #Programming #DataScience #Tech
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Machine Learning
๐ ๏ธ Build Faster, Spend Less. Your All-in-One API Proxy Endpoint. www.afford-ai.cn is designed for developers who need scale without the crazy costs. ๐น 1:2 Value Ratio: Stretch your budget further. For every $1 you fund, we credit your account with $2 inโฆ
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๐ฅ Free IT Cert Resources โ Grab Them While They're Hot!
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๐ฒ Need personalized help? โ https://wa.link/6k7042
โค6
Cheat sheet for Scikit-learn: ๐ Scikit-learn is a Python library for machine learning.
๐ฅ Loading Data - downloading and preparing data.
๐งผ Preprocessing - standardization, normalization, and feature processing.
๐๏ธ Create Your Model - creating models for classification, regression, and clustering.
๐ฏ Model Fitting - training the model on data.
๐ฎ Prediction - obtaining forecasts.
๐ Evaluate Performance - assessing the quality of the model using various metrics.
๐ Cross-Validation - checking the model on different samples.
โ๏ธ Tune Your Model - optimizing parameters using Grid Search and Randomized Search.
#ScikitLearn #MachineLearning #Python #DataScience #AI #MLOps
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๐ฅ Loading Data - downloading and preparing data.
๐งผ Preprocessing - standardization, normalization, and feature processing.
๐๏ธ Create Your Model - creating models for classification, regression, and clustering.
๐ฏ Model Fitting - training the model on data.
๐ฎ Prediction - obtaining forecasts.
๐ Evaluate Performance - assessing the quality of the model using various metrics.
๐ Cross-Validation - checking the model on different samples.
โ๏ธ Tune Your Model - optimizing parameters using Grid Search and Randomized Search.
#ScikitLearn #MachineLearning #Python #DataScience #AI #MLOps
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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 Kaggleโs 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.
๐ Looking for a portfolio-ready NLP project?
I recently published an end-to-end walkthrough on Towards Data Science using Kaggleโs Spooky Author Identification dataset.
Youโll see how far classical NLP can go with:
๐ Bag-of-Words and TF-IDF
๐ค Character n-grams
๐ Model comparison
๐งฉ Ensemble stacking
Itโs 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:
๐ 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 Kaggleโs Spooky Author Identification dataset.
Youโll see how far classical NLP can go with:
๐ Bag-of-Words and TF-IDF
๐ค Character n-grams
๐ Model comparison
๐งฉ Ensemble stacking
Itโs 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:
๐ https://towardsdatascience.com/how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification/
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๐ The Legendary MIT Textbook on Mathematics for Computer Science
Mathematics for Computer Science is one of the best free textbooks for developers, ML engineers, and data scientists.
It contains over 1000 pages covering discrete mathematics, logic, graphs, probability, combinatorics, recurrence relations, and other fundamental topics.
โ๏ธ Link to the textbook:
https://people.csail.mit.edu/meyer/mcs.pdf
#ComputerScience #Mathematics #MachineLearning #DataScience #MIT #OpenSource
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Mathematics for Computer Science is one of the best free textbooks for developers, ML engineers, and data scientists.
It contains over 1000 pages covering discrete mathematics, logic, graphs, probability, combinatorics, recurrence relations, and other fundamental topics.
โ๏ธ Link to the textbook:
https://people.csail.mit.edu/meyer/mcs.pdf
#ComputerScience #Mathematics #MachineLearning #DataScience #MIT #OpenSource
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