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Discover powerful insights with Python, Machine Learning, Coding, and R—your essential toolkit for data-driven solutions, smart alg

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Numpy from basics to advanced.pdf
2.4 MB
📕 Mastering NumPy – From Basics to Advanced

NumPy is an essential library in the world of data science, widely recognized for its efficiency in numerical computations and data manipulation. This powerful tool simplifies complex operations with arrays, offering a faster and cleaner alternative to traditional Python lists and loops.

The "Mastering NumPy" booklet provides a comprehensive walkthrough—from array creation and indexing to mathematical/statistical operations and advanced topics like reshaping and stacking. All concepts are illustrated with clear, beginner-friendly examples, making it ideal for anyone aiming to boost their data handling skills.

#NumPy #Python #DataScience #MachineLearning #AI #BigData #DeepLearning #DataAnalysis


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deep learning book.pdf
14.5 MB
A beautiful booklet for learning deep learning in a smooth and concise way without diving into the world of complexity.

I highly recommend reading this enjoyable booklet.

#DeepLearning #AI #MachineLearning #LearnAI #DeepLearningForBeginners

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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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python_basics.pdf
212.3 KB
🚀 Master Python with Ease!

I've just compiled a set of clean and powerful Python Cheat Sheets to help beginners and intermediates speed up their coding workflow.

Whether you're brushing up on the basics or diving into data science, these sheets will save you time and boost your productivity.

📌 Topics Covered:
Python Basics
Jupyter Notebook Tips
Importing Libraries
NumPy Essentials
Pandas Overview

Perfect for students, developers, and anyone looking to keep essential Python knowledge at their fingertips.

#Python #CheatSheets #PythonTips #DataScience #JupyterNotebook #NumPy #Pandas #MachineLearning #AI #CodingTips #PythonForBeginners

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🔥 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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👫 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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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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