Arcade Academy - Learn Python 🖥

📖 Book

📂 Tags: #DataScience #Python #ML #AI #LLM #Courses #Pandas #DV #GAN

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pandas Project: Make a Gradebook With Python & pandas

Link: https://realpython.com/pandas-project-gradebook/

📂 Tags: #DataScience #Python #ML #AI #LLM #Courses #Pandas #DV #GAN

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Important Methods in #Pandas Package

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Python #Pandas Cheat Sheet 🐼

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming  #Keras

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📄 "Pandas Introduction to Advanced" booklet

👨🏻‍💻 You can't attend a #datascience interview and not be asked about Pandas! But you don't have to memorize all its methods and functions! With this booklet, you'll learn everything you need.

✔️ One of the most useful and interesting combinations is using #Pandas with #AWS Lambda, which can be very useful in real projects.

#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming  #Keras

https://t.me/CodeProgrammer ✅

9 machine learning concepts for ML engineers!

(explained as visually as possible)

Here's a recap of several visual summaries posted in the Daily Dose of Data Science newsletter.

1️⃣ 4 strategies for Multi-GPU Training.

- Training at scale? Learn these strategies to maximize efficiency and minimize model training time.
- Read here: https://lnkd.in/gmXF_PgZ

2️⃣ 4 ways to test models in production

- While testing a model in production might sound risky, ML teams do it all the time, and it isn’t that complicated.
- Implemented here: https://lnkd.in/g33mASMM

3️⃣ Training & inference time complexity of 10 ML algorithms

Understanding the run time of ML algorithms is important because it helps you:
- Build a core understanding of an algorithm.
- Understand the data-specific conditions to use the algorithm
- Read here: https://lnkd.in/gKJwJ__m

4️⃣ Regression & Classification Loss Functions.

- Get a quick overview of the most important loss functions and when to use them.
- Read here: https://lnkd.in/gzFPBh-H

5️⃣ Transfer Learning, Fine-tuning, Multitask Learning, and Federated Learning.

- The holy grail of advanced learning paradigms, explained visually.
- Learn about them here: https://lnkd.in/g2hm8TMT

6️⃣ 15 Pandas to Polars to SQL to PySpark Translations.

- The visual will help you build familiarity with four popular frameworks for data analysis and processing.
- Read here: https://lnkd.in/gP-cqjND

7️⃣ 11 most important plots in data science

- A must-have visual guide to interpret and communicate your data effectively.
- Explained here: https://lnkd.in/geMt98tF

8️⃣ 11 types of variables in a dataset

Understand and categorize dataset variables for better feature engineering.
- Explained here: https://lnkd.in/gQxMhb_p

9️⃣ NumPy cheat sheet for data scientists

- The ultimate cheat sheet for fast, efficient numerical computing in Python.
- Read here: https://lnkd.in/gbF7cJJE

#MachineLearning #DataScience #MLEngineering #DeepLearning #AI #MLOps #BigData #Python #NumPy #Pandas #Visualization

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Python Cheat Sheet

#AI #SentimentAnalysis #DataVisualization #pandas #Numpy #InteractiveDesign #NLP #MachineLearning #Python #GitHubProjects #TowardsDataScience

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🐼 "Comparison Between SQL and pandas" – A Handy Reference Guide

⚡️ As a data scientist, I often found myself switching back and forth between SQL and pandas during technical interviews. I was confident answering questions in SQL but sometimes struggled to translate the same logic into pandas – and vice versa.

🔸 To bridge this gap, I created a concise booklet in the form of a comparison table. It maps SQL queries directly to their equivalent pandas implementations, making it easy to understand and switch between both tools.

⚡ This reference guide has become an essential part of my interview prep. Before any interview, I quickly review it to ensure I’m ready to tackle data manipulation tasks using either SQL or pandas, depending on what’s required.

📕 Whether you're preparing for interviews or just want to solidify your understanding of both tools, this comparison guide is a great way to stay sharp and efficient.

#DataScience #SQL #pandas #InterviewPrep #Python #DataAnalysis #CareerGrowth #TechTips #Analytics

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New to Pandas?

Here's a cheat sheet you can download (2025)

#Pandas #Python #DataAnalysis #PandasCheatSheet #PythonForDataScience #LearnPandas #DataScienceTools #PythonLibraries #FreeResources #DataManipulation

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