Machine Learning with Python

This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

Self-attention in LLMs, clearly explained

#SelfAttention #LLMs #Transformers #NLP #DeepLearning #MachineLearning #AIExplained #AttentionMechanism #AIConcepts #AIEducation

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Supervised Learning: Classification and Regression Download: https://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176/lecturenotes.pdf

#SupervisedLearning #MachineLearning #Classification #Regression #MLNotes #DataScience #AIResources #MLTheory #MLLectures #LearnML

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Anyone trying to deeply understand Large Language Models. Checkout

Foundations of Large Language Models

by Tong Xiao & Jingbo Zhu. It’s one of the clearest, most comprehensive resource. ⭐️ Paper Link: arxiv.org/pdf/2501.09223

#LLMs #LargeLanguageModels #AIResearch #DeepLearning #MachineLearning #AIResources #NLP #AITheory #FoundationModels #AIUnderstanding



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👫 Preparing for Data Science Interviews 👨🏻‍💻 I've been collecting a variety of data science interview questions for different positions for a few weeks now. ✅ I covered everything, from basic to advanced:

Common Data Science and ML Questions (34 questions)

Regression (22 questions)

Classification (39 questions)

SVM algorithms, decision tree

Simple Bayes and statistical discussions and...

🚨 This list is regularly updated and categorized so that you can easily prepare for the interview step by step.👇 ┌📝 Interview Questions └🐱 GitHub-Repos

#DataScience #InterviewPrep #MLInterviews #DataScientist #MachineLearning #TechCareers #DSInterviewQuestions #GitHubResources #CareerInDataScience #CodingInterview

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This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

🔥 How to become a data scientist in 2025? 1️⃣ First of all, strengthen your foundation (math and statistics) . ✏️ If you don't know math, you'll run into trouble wherever you go. Every model you build, every analysis you do, there's a world of math behind it. You need to know these things well: ✅ Linear Algebra: Link ✅ Calculus: Link ✅ Statistics and Probability: Link ➖➖➖➖➖➖ 2️⃣ Then learn programming ! ✏️ Without further ado, get started learning Python and SQL. ✅ Python: Link ✅ SQL language: Link ✅ Data Structures and Algorithms: Link ➖➖➖➖➖➖ 3️⃣ Learn to clean and analyze data! ✏️ Data is always messy, and a data scientist must know how to organize it and extract insights from it. ✅ Data cleansing: Link ✅ Data visualization: Link ➖➖➖➖➖➖ 4️⃣ Learn machine learning ! ✏️ Once you've mastered the basic skills, it's time to enter the world of machine learning. Here's what you need to know: ◀️ Supervised learning: regression, classification ◀️ Unsupervised learning: clustering, dimensionality reduction ◀️ Deep learning: neural networks, CNN, RNN ✅ Stanford University CS229 course: Link ➖➖➖➖➖➖ 5️⃣ Get to know big data and cloud computing ! ✏️ Large companies are looking for people who can work with large volumes of data. ◀️ Big data tools (e.g. Hadoop, Spark, Dask) ◀️ Cloud services (AWS, GCP, Azure) ➖➖➖➖➖➖ 6️⃣ Do a real project and build a portfolio ! ✏️ Everything you've learned so far is worthless without a real project! ◀️ Participate in Kaggle and work with real data. ◀️ Do a project from scratch (from data collection to model deployment) ◀️ Put your code on GitHub. ✅ Open Source Data Science Projects: Link ➖➖➖➖➖➖ 7️⃣ It's time to learn MLOps and model deployment! ✏️ Many people just build models but don't know how to deploy them. But companies want someone who can put the model into action! ◀️ Machine learning operationalization (monitoring, updating models) ◀️ Model deployment tools: Flask, FastAPI, Docker ✅ Stanford University MLOps Course: Link ➖➖➖➖➖➖ 8️⃣ Always stay up to date and network! ✏️ Follow research articles on arXiv and Google Scholar. ✅ Papers with Code website: link ✅ AI Research at Google website: link

#DataScience #HowToBecomeADataScientist #ML2025 #Python #SQL #MachineLearning #MathForDataScience #BigData #MLOps #DeepLearning #AIResearch #DataVisualization #PortfolioProjects #CloudComputing #DSCareerPath



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🚀 DataCamp has officially partnered with Polars**—a cutting-edge DataFrame library designed for speed and efficiency! To mark this exciting collaboration, **DataCamp is offering free access to its brand-new course *“Introduction to Polars”* for the next 90 days. 🎉 This course is a great opportunity for learners and professionals alike to master data cleaning, transformation, and analysis with Polars' high-performance engine, lazy execution, and powerful groupby operations. Unlock the full potential of data workflows and explore how Polars can supercharge large-scale data processing. 🔗 Start learning now: https://www.datacamp.com/courses/introduction-to-polars

#DataScience #Polars #Python #BigData #DataEngineering #MachineLearning #DataAnalytics #OpenSource #DataCamp #FreeCourse #LearnDataScience

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