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Python | Machine Learning | Coding | R

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Python | Machine Learning | Coding | R

import_data.pdf

numpy_basics.pdf

pandas_basics.pdf

python_basics.pdf

🚀

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.

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Python | Machine Learning | Coding | R

🔥 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

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Then learn programming !

✏️ Without further ado, get started learning Python and SQL.

✅ Python: Link

✅ SQL language: Link

✅ Data Structures and Algorithms: Link

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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

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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:

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Supervised learning: regression, classification

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Unsupervised learning: clustering, dimensionality reduction

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Deep learning: neural networks, CNN, RNN

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Stanford University CS229 course: Link

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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)

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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

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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

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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

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Python | Machine Learning | Coding | R

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Python | Machine Learning | Coding | R

Python | Machine Learning | Coding | R pinned a photo

12:30

Python | Machine Learning | Coding | R

👫

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.👇

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Interview Questions
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Python | Machine Learning | Coding | R

📖 A comprehensive cheat sheet for working with Polars

🌟 Have you ever worked with pandas and thought that was the fastest way? I thought the same thing until I worked with Polars.

✏️ This cheat sheet explains everything about Polars in a concise and simple way. Not just theory! But also a bunch of real examples, practical experience, and projects that will really help you in the real world.

┌ 🐻‍❄️ Polars Cheat Sheet
├ ♾️ Google Colab
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Python | Machine Learning | Coding | R

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

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7.15K viewsedited 06:35

Python | Machine Learning | Coding | R

Supervised Learning: Classification and Regression

Download: https://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176/lecturenotes.pdf

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Python | Machine Learning | Coding | R

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08:52

Python | Machine Learning | Coding | R

Self-attention in LLMs, clearly explained

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

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Python | Machine Learning | Coding | R

Forwarded from Python | Machine Learning | Coding | R

This channels is for Programmers, Coders, Software Engineers.

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Python | Machine Learning | Coding | R

🥇 40+ Real and Free Data Science Projects

👨🏻‍💻 Real learning means implementing ideas and building prototypes. It's time to skip the repetitive training and get straight to real data science projects!

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Python | Machine Learning | Coding | R

Forwarded from Data Science Jupyter Notebooks

🐍Looking to get started with Deep Learning using PyTorch?

This well-structured GitHub repository is a goldmine for beginners who want to learn PyTorch with hands-on examples and clear explanations📖.

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🈂 Jupyter Notebooks with interactive code.
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Python | Machine Learning | Coding | R

Mathematics for Computer Science

Book Details

- Discrete Mathematics: An Open Introduction
- By Oscar Levin
- 2025 Edition
- 547 pages

🔗 Download the Book
discrete.openmathbooks.org/pdfs/dmoi4.pdf

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Python | Machine Learning | Coding | R

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Python | Machine Learning | Coding | R

Get important resources from books and courses

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Python | Machine Learning | Coding | R

Step-by-Step Guide to Deploying Machine Learning Models with FastAPI and Docker

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Python | Machine Learning | Coding | R

𝗬𝗼𝘂𝗿_𝗗𝗮𝘁𝗮_𝗦𝗰𝗶𝗲𝗻𝗰𝗲_𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄_𝗦𝘁𝘂𝗱𝘆_𝗣𝗹𝗮𝗻.pdf

1. Master the fundamentals of Statistics

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Construct end-to-end data pipelines

Develop predictive models with machine learning

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4. Practice case study interviews

Learn to break down ambiguous business problems

Ask clarifying questions to gather requirements

Think aloud and structure your answers logically

5. Mock interviews with feedback

Use platforms like Pramp or connect with peers

Record and review your answers for improvement

Gather feedback on your explanation and presence

6. Revise machine learning concepts

Understand supervised vs unsupervised learning

Grasp overfitting, underfitting, and bias-variance tradeoff

Know how to evaluate models (precision, recall, F1-score, AUC, etc.)

7. Brush up on system design (if applicable)

Learn how to design scalable data pipelines

Compare real-time vs batch processing

Familiarize with tools: Apache Spark, Kafka, Airflow

8. Strengthen storytelling with data

Apply the STAR method in behavioral questions

Simplify complex technical topics

Emphasize business impact and insight-driven decisions

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Tailor your resume for each job role

Include links to projects or GitHub profiles

Match your skills to job descriptions

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Set clear weekly goals

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Python | Machine Learning | Coding | R

🔍 Understanding Recurrent Neural Networks (RNNs) Cheat Sheet!
Recurrent Neural Networks are a powerful type of neural network designed to handle sequential data. They are widely used in applications like natural language processing, speech recognition, and time-series prediction. Here's a quick cheat sheet to get you started:

📘 Key Concepts:
Sequential Data: RNNs are designed to process sequences of data, making them ideal for tasks where order matters.
Hidden State: Maintains information from previous inputs, enabling memory across time steps.
Backpropagation Through Time (BPTT): The method used to train RNNs by unrolling the network through time.

🔧 Common Variants:
Long Short-Term Memory (LSTM): Addresses vanishing gradient problems with gates to manage information flow.
Gated Recurrent Unit (GRU): Similar to LSTMs but with a simpler architecture.

🚀 Applications:
Language Modeling: Predicting the next word in a sentence.
Sentiment Analysis: Understanding sentiments in text.
Time-Series Forecasting: Predicting future data points in a series.

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Dive deeper with tutorials on platforms like Coursera, edX, or YouTube.
Explore open-source libraries like TensorFlow or PyTorch for implementation.
Let's harness the power of RNNs to innovate and solve complex problems! 💡

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Python | Machine Learning | Coding | R

Forwarded from Python Data Science Jobs & Interviews

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