Forwarded from Machine Learning
📌 Your First 90 Days as a Data Scientist
🗂 Category: DATA SCIENCE
🕒 Date: 2026-02-14 | ⏱️ Read time: 8 min read
A practical onboarding checklist for building trust, business fluency, and data intuition
#DataScience #AI #Python
🗂 Category: DATA SCIENCE
🕒 Date: 2026-02-14 | ⏱️ Read time: 8 min read
A practical onboarding checklist for building trust, business fluency, and data intuition
#DataScience #AI #Python
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Forwarded from Python Courses & Resources
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Data scientists are in high demand right now: there's just too much data to analyze.
In this course, Tatev and Vae teach #Python for #DataScience.
You'll be doing projects and exploring EDA, A/B testing, BI, and more.
https://xn--r1a.website/Python53🌟
In this course, Tatev and Vae teach #Python for #DataScience.
You'll be doing projects and exploring EDA, A/B testing, BI, and more.
https://xn--r1a.website/Python53
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Data Science Roadmap.pdf
15.5 MB
🏷 Comprehensive Data Science Roadmap Notes
✅ This roadmap is exactly the secret recipe you need to get out of confusion and know how to step-by-step prepare yourself for the job market.
🕡 From mastering Python and SQL to cleaning data and working with cloud tools, which are prerequisites for any project.
🕑 How to extract real analysis reports and strategies from raw data using statistics and visualization tools.
🕗 You will learn everything from machine learning and advanced algorithms to precise model evaluation.
🕙 Get familiar with neural networks, generative artificial intelligence, and language models to have a voice in today's modern world.
🕧 How to build real projects and portfolios that are exactly what hiring managers and big companies are looking for.
🌐 #DataScience #DataScience #pytorch #python #Roadmap
https://xn--r1a.website/CodeProgrammer
✅ This roadmap is exactly the secret recipe you need to get out of confusion and know how to step-by-step prepare yourself for the job market.
🕡 From mastering Python and SQL to cleaning data and working with cloud tools, which are prerequisites for any project.
🕑 How to extract real analysis reports and strategies from raw data using statistics and visualization tools.
🕗 You will learn everything from machine learning and advanced algorithms to precise model evaluation.
🕙 Get familiar with neural networks, generative artificial intelligence, and language models to have a voice in today's modern world.
🕧 How to build real projects and portfolios that are exactly what hiring managers and big companies are looking for.
🌐 #DataScience #DataScience #pytorch #python #Roadmap
https://xn--r1a.website/CodeProgrammer
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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 – 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 – 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://xn--r1a.website/CodeProgrammer
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🗂 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.
➡️ 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.
➡️ Link to the course
tags: #Python #DataScience #DeepLearning #AI
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The matrix cookbook.pdf
676.5 KB
📚 Notes and Important Formulas ⬅️ "Matrices, Linear Algebra, and Probability"
👨🏻💻 This booklet serves as an essential resource for individuals initiating their studies in data science. It consolidates comprehensive information on matrices, linear algebra, and probability, thereby eliminating the necessity of consulting multiple sources.
✏️ The document encompasses nearly all pertinent formulas and key concepts. It addresses foundational topics such as determinants and matrix inverses, as well as advanced subjects including eigenvalues, eigenvectors, Singular Value Decomposition (SVD), and probability distributions.
🌐 #DataScience #Python #Math
https://xn--r1a.website/CodeProgrammer🌟
👨🏻💻 This booklet serves as an essential resource for individuals initiating their studies in data science. It consolidates comprehensive information on matrices, linear algebra, and probability, thereby eliminating the necessity of consulting multiple sources.
✏️ The document encompasses nearly all pertinent formulas and key concepts. It addresses foundational topics such as determinants and matrix inverses, as well as advanced subjects including eigenvalues, eigenvectors, Singular Value Decomposition (SVD), and probability distributions.
https://xn--r1a.website/CodeProgrammer
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A good selection for those who want to improve their skills in practice, rather than just reading theory:
tags: #ML #DataScience #DataAnalysis
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❤7💯2
This Machine Learning Cheat Sheet Saved Me Hours of Revision ⏳
It includes:
✅ Supervised & Unsupervised algorithms
✅ Regression, Classification & Clustering techniques
✅ PCA & Dimensionality Reduction
✅ Neural Networks, CNN, RNN & Transformers
✅ Assumptions, Pros/Cons & Real-world use cases
Whether you're:
🔹 Preparing for data science interviews
🔹 Working on ML projects
🔹 Or strengthening your fundamentals
this one-page guide is a must-save.
♻️ Repost and share with your ML circle.
#MachineLearning #DataScience #AI #MLAlgorithms #InterviewPrep #LearnML
https://xn--r1a.website/CodeProgrammer🐍
It includes:
✅ Supervised & Unsupervised algorithms
✅ Regression, Classification & Clustering techniques
✅ PCA & Dimensionality Reduction
✅ Neural Networks, CNN, RNN & Transformers
✅ Assumptions, Pros/Cons & Real-world use cases
Whether you're:
🔹 Preparing for data science interviews
🔹 Working on ML projects
🔹 Or strengthening your fundamentals
this one-page guide is a must-save.
♻️ Repost and share with your ML circle.
#MachineLearning #DataScience #AI #MLAlgorithms #InterviewPrep #LearnML
https://xn--r1a.website/CodeProgrammer
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🔖 Interactive textbook on probability theory and statistics 📊✨
A super-intuitive site where you can visually study distributions, sampling, and statistical concepts. 📈🎲
No tons of formulas and boring theory — everything is demonstrated through interactive examples and simulations. 💻🔬
⛓️ Download here 👇
https://seeing-theory.brown.edu/
#Probability #Statistics #DataScience #Learning #Interactive #Math
https://xn--r1a.website/CodeProgrammer
A super-intuitive site where you can visually study distributions, sampling, and statistical concepts. 📈🎲
No tons of formulas and boring theory — everything is demonstrated through interactive examples and simulations. 💻🔬
⛓️ Download here 👇
https://seeing-theory.brown.edu/
#Probability #Statistics #DataScience #Learning #Interactive #Math
https://xn--r1a.website/CodeProgrammer
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Forwarded from Learn Python Coding
Cheat sheet on the basics of Python: 🐍📚
basic syntax and language rules 📝
scalar types — basic data types (int, float, bool, str, NoneType) 🔢
datetime — working with date and time 📅⏰
data structures — Python data structures (list, tuple, dict, set) 🗄
list — mutable lists for storing data collections 📋
tuple — immutable sequences of values 🔒
dict (hash map) — storing data in a key-value format 🗝
set — unique elements without order 🔘
slicing — obtaining parts of sequences through indices and step ✂️
module/library — connecting modules and libraries 🔌
help functions — using help() and dir() to explore the Python API 🛠
#Python #Coding #DataScience #Programming #Tech #DevCommunity
basic syntax and language rules 📝
scalar types — basic data types (int, float, bool, str, NoneType) 🔢
datetime — working with date and time 📅⏰
data structures — Python data structures (list, tuple, dict, set) 🗄
list — mutable lists for storing data collections 📋
tuple — immutable sequences of values 🔒
dict (hash map) — storing data in a key-value format 🗝
set — unique elements without order 🔘
slicing — obtaining parts of sequences through indices and step ✂️
module/library — connecting modules and libraries 🔌
help functions — using help() and dir() to explore the Python API 🛠
#Python #Coding #DataScience #Programming #Tech #DevCommunity
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Forwarded from Machine Learning
🚀 Master Binary Classification with Neural Networks! 🧠✨
Ever wondered how to build a neural network from scratch in Python using NumPy? 🐍📊
Binary classification is at the heart of many machine learning applications. 🎯🤖
Our super-detailed guide walks you through the entire process step by step. 📝📚
💡 Dive in and start building your own neural network today! 🏗🔥
https://tinztwinshub.com/data-science/a-beginners-guide-to-developing-an-artificial-neural-network-from-zero/
#MachineLearning #NeuralNetworks #Python #DataScience #AI #Tech
Ever wondered how to build a neural network from scratch in Python using NumPy? 🐍📊
Binary classification is at the heart of many machine learning applications. 🎯🤖
Our super-detailed guide walks you through the entire process step by step. 📝📚
💡 Dive in and start building your own neural network today! 🏗🔥
https://tinztwinshub.com/data-science/a-beginners-guide-to-developing-an-artificial-neural-network-from-zero/
#MachineLearning #NeuralNetworks #Python #DataScience #AI #Tech
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Forwarded from Machine Learning
🔥 Awesome open-source project to learn more about Transformer Models! 🤖✨
We found this interactive website that shows you visually how transformer models work. 🌐📊
Transformer Explainer:
https://poloclub.github.io/transformer-explainer/
#TransformerModels #OpenSource #AI #MachineLearning #DataScience #Tech
We found this interactive website that shows you visually how transformer models work. 🌐📊
Transformer Explainer:
https://poloclub.github.io/transformer-explainer/
#TransformerModels #OpenSource #AI #MachineLearning #DataScience #Tech
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Forwarded from Data Analytics
Pandas vs Polars vs DuckDB: Which Library Should You Choose? 🤔📊
pandas remains the default choice for notebooks, exploratory analysis, visualization, and machine learning workflows 📝📈. Polars focus on fast, memory-efficient DataFrame processing ⚡💾, while DuckDB brings a SQL-first approach for querying local files and embedded analytics 🗄️🔍.
Each tool fits a different kind of local data workflow 🛠️. In this article, we compare pandas, Polars, and DuckDB across performance, architecture, interoperability, and real-world use cases 🏆🔗.
More: https://www.analyticsvidhya.com/blog/2026/05/pandas-vs-polars-vs-duckdb/ 🔗
#DataScience #Pandas #Polars #DuckDB #Python #Analytics
pandas remains the default choice for notebooks, exploratory analysis, visualization, and machine learning workflows 📝📈. Polars focus on fast, memory-efficient DataFrame processing ⚡💾, while DuckDB brings a SQL-first approach for querying local files and embedded analytics 🗄️🔍.
Each tool fits a different kind of local data workflow 🛠️. In this article, we compare pandas, Polars, and DuckDB across performance, architecture, interoperability, and real-world use cases 🏆🔗.
More: https://www.analyticsvidhya.com/blog/2026/05/pandas-vs-polars-vs-duckdb/ 🔗
#DataScience #Pandas #Polars #DuckDB #Python #Analytics
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Found an easy way to learn math for ML: Mathematics for Machine Learning 🎓📚
This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊
It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖
Free public repository on GitHub. 💻✨
https://github.com/dair-ai/Mathematics-for-ML
#MachineLearning #Mathematics #DataScience #Learning #GitHub #AI
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https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊
It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖
Free public repository on GitHub. 💻✨
https://github.com/dair-ai/Mathematics-for-ML
#MachineLearning #Mathematics #DataScience #Learning #GitHub #AI
✨ Join Best TG Channels
https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel
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GitHub
GitHub - dair-ai/Mathematics-for-ML: 🧮 A collection of resources to learn mathematics for machine learning
🧮 A collection of resources to learn mathematics for machine learning - dair-ai/Mathematics-for-ML
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Stop discovering ML Python libraries one random tutorial at a time 🛑
Best-of Machine Learning with Python is a curated GitHub index of open-source machine learning Python libraries for builders who need a faster way to compare the ecosystem 📚.
It helps you shortlist tools by grouping projects into categories and ranking them with a project-quality score based on metrics collected from GitHub and package managers 📊.
Key features:
• 920-project index – a large scan-friendly map of open-source ML Python projects 🗺️
• 34 categories – browse by area like ML frameworks, NLP, image data, AutoML, deployment, interpretability, and more 🧩
• Quality-score ranking – projects are ordered using an automated score from repo and package-manager signals ⚙️
• Rich project metadata – entries show signals like stars, forks, issues, contributors, activity, downloads, and dependencies 📈
• Weekly updates + contributions – the list is updated regularly and can be improved via issues, PRs, or projects.yaml edits 🔄
It’s open-source (CC BY-SA 4.0 license) 📜.
https://github.com/lukasmasuch/best-of-ml-python 🔗
#MachineLearning #Python #ML #OpenSource #DataScience #TechStack
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Best-of Machine Learning with Python is a curated GitHub index of open-source machine learning Python libraries for builders who need a faster way to compare the ecosystem 📚.
It helps you shortlist tools by grouping projects into categories and ranking them with a project-quality score based on metrics collected from GitHub and package managers 📊.
Key features:
• 920-project index – a large scan-friendly map of open-source ML Python projects 🗺️
• 34 categories – browse by area like ML frameworks, NLP, image data, AutoML, deployment, interpretability, and more 🧩
• Quality-score ranking – projects are ordered using an automated score from repo and package-manager signals ⚙️
• Rich project metadata – entries show signals like stars, forks, issues, contributors, activity, downloads, and dependencies 📈
• Weekly updates + contributions – the list is updated regularly and can be improved via issues, PRs, or projects.yaml edits 🔄
It’s open-source (CC BY-SA 4.0 license) 📜.
https://github.com/lukasmasuch/best-of-ml-python 🔗
#MachineLearning #Python #ML #OpenSource #DataScience #TechStack
✨ Join Best TG Channels https://xn--r1a.website/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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