𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 𝗟𝗶𝗸𝗲 𝗮 𝗣𝗿𝗼 – 𝗔𝗹𝗹-𝗶𝗻-𝗢𝗻𝗲 𝗚𝘂𝗶𝗱𝗲 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀

If you're a data engineer, aspiring Spark developer, or someone preparing for big data interviews — this one is for you.
I’m sharing a powerful, all-in-one PySpark notes sheet that covers both fundamentals and advanced techniques for real-world usage and interviews.

𝗪𝗵𝗮𝘁'𝘀 𝗶𝗻𝘀𝗶𝗱𝗲? • Spark vs MapReduce
• Spark Architecture – Driver, Executors, DAG
• RDDs vs DataFrames vs Datasets
• SparkContext vs SparkSession
• Transformations: map, flatMap, reduceByKey, groupByKey
• Optimizations – caching, persisting, skew handling, salting
• Joins – Broadcast joins, Shuffle joins
• Deployment modes – Cluster vs Client
• Real interview-ready Q&A from top use cases
• CSV, JSON, Parquet, ORC – Format comparisons
• Common commands, schema creation, data filtering, null handling

𝗪𝗵𝗼 𝗶𝘀 𝘁𝗵𝗶𝘀 𝗳𝗼𝗿? Data Engineers, Spark Developers, Data Enthusiasts, and anyone preparing for interviews or working on distributed systems.

#PySpark #DataEngineering #BigData #SparkArchitecture #RDDvsDataFrame #SparkOptimization #DistributedComputing #SparkInterviewPrep #DataPipelines #ApacheSpark #MapReduce #ETL #BroadcastJoin #ClusterComputing #SparkForEngineers

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📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🐍📰 This tutorial will give you an overview of LangGraph fundamentals through hands-on examples, and the tools needed to build your own LLM workflows and agents in LangGraph

Link: https://realpython.com/langgraph-python/

#LangGraph #Python #LLMWorkflows #AIAgents #RealPython #PythonTutorials #LargeLanguageModels #AIAgents #WorkflowAutomation #PythonForA

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📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

A curated collection of Kaggle notebooks showcasing how to build end-to-end AI applications using Hugging Face pretrained models, covering text, speech, image, and vision-language tasks — full tutorials and code available on GitHub:

1️⃣ Text-Based Applications

1.1. Building a Chatbot Using HuggingFace Open Source Models

https://lnkd.in/dku3bigK

1.2. Building a Text Translation System using Meta NLLB Open-Source Model

https://lnkd.in/dgdjaFds

2️⃣ Speech-Based Applications

2.1. Zero-Shot Audio Classification Using HuggingFace CLAP Open-Source Model

https://lnkd.in/dbgQgDyn

2.2. Building & Deploying a Speech Recognition System Using the Whisper Model & Gradio

https://lnkd.in/dcbp-8fN

2.3. Building Text-to-Speech Systems Using VITS & ArTST Models

https://lnkd.in/dwFcQ_X5

3️⃣ Image-Based Applications

3.1. Step-by-Step Guide to Zero-Shot Image Classification using CLIP Model

https://lnkd.in/dnk6epGB

3.2. Building an Object Detection Assistant Application: A Step-by-Step Guide

https://lnkd.in/d573SvYV

3.3. Zero-Shot Image Segmentation using Segment Anything Model (SAM)

https://lnkd.in/dFavEdHS

3.4. Building Zero-Shot Depth Estimation Application Using DPT Model & Gradio

https://lnkd.in/d9jjJu_g

4️⃣ Vision Language Applications

4.1. Building a Visual Question Answering System Using Hugging Face Open-Source Models

https://lnkd.in/dHNFaHFV

4.2. Building an Image Captioning System using Salesforce Blip Model

https://lnkd.in/dh36iDn9

4.3. Building an Image-to-Text Matching System Using Hugging Face Open-Source Models

https://lnkd.in/d7fsJEAF

➡️ You can find the articles and the codes for each article in this GitHub repo:

https://lnkd.in/dG5jfBwE

#HuggingFace #Kaggle #AIapplications #DeepLearning #MachineLearning #ComputerVision #NLP #SpeechRecognition #TextToSpeech #ImageProcessing #OpenSourceAI #ZeroShotLearning #Gradio

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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://www.tgoop.com/addlist/8_rRW2scgfRhOTc0

✅ https://www.tgoop.com/Codeprogrammer

The 2025 MIT deep learning course is excellent, covering neural networks, CNNs, RNNs, and LLMs. You build three projects for hands-on experience as part of the course. It is entirely free. Highly recommended for beginners.

Enroll Free: https://introtodeeplearning.com/

#DeepLearning #MITCourse #NeuralNetworks #CNN #RNN #LLMs #AIForBeginners #FreeCourse #MachineLearning #IntroToDeepLearning #AIProjects #LearnAI #AI2025

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📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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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Top 50 LLM Interview Questions!

A comprehensive resource that covers traditional ML basics, model architectures, real-world case studies, and theoretical foundations.

👇👇👇👇👇👇

✉️ Our Telegram channels: https://www.tgoop.com/addlist/0f6vfFbEMdAwODBk

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Top 50 LLM Interview Questions!

#LLM #AIInterviews #MachineLearning #DeepLearning #NLP #LLMInterviewPrep #ModelArchitectures #AITheory #TechInterviews #MLBasics #InterviewQuestions #LargeLanguageModels

✉️ Our Telegram channels: https://www.tgoop.com/addlist/0f6vfFbEMdAwODBk

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This book covers foundational topics within computer vision, with an image processing and machine learning perspective. We want to build the reader’s intuition and so we include many visualizations. The audience is undergraduate and graduate students who are entering the field, but we hope experienced practitioners will find the book valuable as well.

Our initial goal was to write a large book that provided a good coverage of the field. Unfortunately, the field of computer vision is just too large for that. So, we decided to write a small book instead, limiting each chapter to no more than five pages. Such a goal forced us to really focus on the important concepts necessary to understand each topic. Writing a short book was perfect because we did not have time to write a long book and you did not have time to read it. Unfortunately, we have failed at that goal, too.

Read it online: https://visionbook.mit.edu/

#ComputerVision #ImageProcessing #MachineLearning #CVBook #VisualLearning #AIResources #ComputerVisionBasics #MLForVision #AcademicResources #LearnComputerVision #AIIntuition #DeepLearning

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Machine learning and deep learning
✅@Machine_learn

Large language Model Git

🔺https://www.tgoop.com/deep_learning_proj

Over the last year, several articles have been written to help candidates prepare for data science technical interviews. These resources cover a wide range of topics including machine learning, SQL, programming, statistics, and probability.

1️⃣ Machine Learning (ML) Interview
Types of ML Q&A in Data Science Interview
https://shorturl.at/syN37

ML Interview Q&A for Data Scientists
https://shorturl.at/HVWY0

Crack the ML Coding Q&A
https://shorturl.at/CDW08

Deep Learning Interview Q&A
https://shorturl.at/lHPZ6

Top LLMs Interview Q&A
https://shorturl.at/wGRSZ

Top CV Interview Q&A [Part 1]
https://rb.gy/51jcfi

Part 2
https://rb.gy/hqgkbg

Part 3
https://rb.gy/5z87be

2️⃣ SQL Interview Preparation
13 SQL Statements for 90% of Data Science Tasks
https://rb.gy/dkdcl1

SQL Window Functions: Simplifying Complex Queries
https://t.ly/EwSlH

Ace the SQL Questions in the Technical Interview
https://lnkd.in/gNQbYMX9

Unlocking the Power of SQL: How to Ace Top N Problem Questions
https://lnkd.in/gvxVwb9n

How To Ace the SQL Ratio Problems
https://lnkd.in/g6JQqPNA

Cracking the SQL Window Function Coding Questions
https://lnkd.in/gk5u6hnE

SQL & Database Interview Q&A
https://lnkd.in/g75DsEfw

6 Free Resources for SQL Interview Preparation
https://lnkd.in/ghhiG79Q

3️⃣ Programming Questions
Foundations of Data Structures [Part 1]
https://lnkd.in/gX_ZcmRq

Part 2
https://lnkd.in/gATY4rTT

Top Important Python Questions [Conceptual]
https://lnkd.in/gJKaNww5

Top Important Python Questions [Data Cleaning and Preprocessing]
https://lnkd.in/g-pZBs3A

Top Important Python Questions [Machine & Deep Learning]
https://lnkd.in/gZwcceWN

Python Interview Q&A
https://lnkd.in/gcaXc_JE

5 Python Tips for Acing DS Coding Interview
https://lnkd.in/gsj_Hddd

4️⃣ Statistics
Mastering 5 Statistics Concepts to Boost Success
https://lnkd.in/gxEuHiG5

Mastering Hypothesis Testing for Interviews
https://lnkd.in/gSBbbmF8

Introduction to A/B Testing
https://lnkd.in/g35Jihw6

Statistics Interview Q&A for Data Scientists
https://lnkd.in/geHCCt6Q

5️⃣ Probability
15 Probability Concepts to Review [Part 1]
https://lnkd.in/g2rK2tQk

Part 2
https://lnkd.in/gQhXnKwJ

Probability Interview Q&A [Conceptual Questions]
https://lnkd.in/g5jyKqsp

Probability Interview Q&A [Mathematical Questions]
https://lnkd.in/gcWvPhVj

🔜 All links are available in the GitHub repository:
https://lnkd.in/djcgcKRT

#DataScience #InterviewPrep #MachineLearning #SQL #Python #Statistics #Probability #CodingInterview #AIBootcamp #DeepLearning #LLMs #ComputerVision #GitHubResources #CareerInDataScience

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10 GitHub repos to build a career in AI engineering:

(100% free step-by-step roadmap)

1️⃣ ML for Beginners by Microsoft

A 12-week project-based curriculum that teaches classical ML using Scikit-learn on real-world datasets.

Includes quizzes, lessons, and hands-on projects, with some videos.

GitHub repo → https://lnkd.in/dCxStbYv

2️⃣ AI for Beginners by Microsoft

This repo covers neural networks, NLP, CV, transformers, ethics & more. There are hands-on labs in PyTorch & TensorFlow using Jupyter.

Beginner-friendly, project-based, and full of real-world apps.

GitHub repo → https://lnkd.in/dwS5Jk9E

3️⃣ Neural Networks: Zero to Hero

Now that you’ve grasped the foundations of AI/ML, it’s time to dive deeper.

This repo by Andrej Karpathy builds modern deep learning systems from scratch, including GPTs.

GitHub repo → https://lnkd.in/dXAQWucq

4️⃣ DL Paper Implementations

So far, you have learned the fundamentals of AI, ML, and DL. Now study how the best architectures work.

This repo covers well-documented PyTorch implementations of 60+ research papers on Transformers, GANs, Diffusion models, etc.

GitHub repo → https://lnkd.in/dTrtDrvs

5️⃣ Made With ML

Now it’s time to learn how to go from notebooks to production.

Made With ML teaches you how to design, develop, deploy, and iterate on real-world ML systems using MLOps, CI/CD, and best practices.

GitHub repo → https://lnkd.in/dYyjjBGb

6️⃣ Hands-on LLMs

- You've built neural nets.
- You've explored GPTs and LLMs.

Now apply them. This is a visually rich repo that covers everything about LLMs, like tokenization, fine-tuning, RAG, etc.

GitHub repo → https://lnkd.in/dh2FwYFe

7️⃣ Advanced RAG Techniques

Hands-on LLMs will give you a good grasp of RAG systems. Now learn advanced RAG techniques.

This repo covers 30+ methods to make RAG systems faster, smarter, and accurate, like HyDE, GraphRAG, etc.

GitHub repo → https://lnkd.in/dBKxtX-D

8️⃣ AI Agents for Beginners by Microsoft

After diving into LLMs and mastering RAG, learn how to build AI agents.

This hands-on course covers building AI agents using frameworks like AutoGen.

GitHub repo → https://lnkd.in/dbFeuznE

9️⃣ Agents Towards Production

The above course will teach what AI agents are. Next, learn how to ship them.

This is a practical playbook for building agents covering memory, orchestration, deployment, security & more.

GitHub repo → https://lnkd.in/dcwmamSb

🔟 AI Engg. Hub

To truly master LLMs, RAG, and AI agents, you need projects.

This covers 70+ real-world examples, tutorials, and agent app you can build, adapt, and ship.

GitHub repo → https://lnkd.in/geMYm3b6

#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers

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Master MCP: The Best Free Learning Resources

1️⃣ Everything you need to know about MCP: The first learning resource is a beginner-friendly introduction to MCP by Replit
https://lnkd.in/djVD73Gz

2️⃣ Model Context Protocol (MCP): A Guide With Demo Project: In this blog, you will be guided through building an MCP-powered PR review server that integrates with Claude Desktop
https://lnkd.in/dXDNbAat

3️⃣ Model Context Protocol (MCP) Hugging Face Course: This free course will take you on a journey, from beginner to informed, in understanding, using, and building applications with MCP
https://lnkd.in/dX5Ja_9m

4️⃣ MCP: Build Rich-Context AI Apps with Anthropic: In this hands-on course, you’ll learn the core concepts of MCP and how to implement it in your AI Application
https://lnkd.in/dxRyjRiW

5️⃣ Official MCP Documents: The official MCP docs are a good resource to learn the fundamentals, a tutorial to create your first MCP server, debugging, and inspection instructions
https://lnkd.in/dqkQ6e_b

6️⃣ Awesome MCP Servers: A curated list of awesome Model Context Protocol (MCP) servers
https://lnkd.in/d2AvkBmb

🌟 You can find more information about each learning resource in this article:
https://lnkd.in/dbDHJnNi

#MCP #ModelContextProtocol #AIApplications #ContextAwareAI #MCPLearning #Anthropic #HuggingFace #Replit #AIIntegration #AIFrameworks #OpenSourceAI #LearnMCP #AIEngineering #PromptEngineering #AIProtocols

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A comprehensive PDF has been compiled that includes all MCP-related posts shared over the past six months.

(75 pages, 10+ projects & visual explainers)

Over the last half year, content has been published about the Modular Computation Protocol (MCP), which has gained significant interest and engagement from the AI community. In response to this enthusiasm, all tutorials have been gathered in one place, featuring:

* The fundamentals of MCP
* Explanations with visuals and code
* 11 hands-on projects for AI engineers

Projects included:

1. Build a 100% local MCP Client
2. MCP-powered Agentic RAG
3. MCP-powered Financial Analyst
4. MCP-powered Voice Agent
5. A Unified MCP Server
6. MCP-powered Shared Memory for Claude Desktop and Cursor
7. MCP-powered RAG over Complex Docs
8. MCP-powered Synthetic Data Generator
9. MCP-powered Deep Researcher
10. MCP-powered RAG over Videos
11. MCP-powered Audio Analysis Toolkit

#MCP #ModularComputationProtocol #AIProjects #DeepLearning #ArtificialIntelligence #RAG #VoiceAI #SyntheticData #AIAgents #AIResearch #TechWriting #OpenSourceAI #AI #python

✉️ Our Telegram channels: https://www.tgoop.com/addlist/0f6vfFbEMdAwODBk

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Free Courses from top companies 🤓

AI (Microsoft) -
https://learn.microsoft.com/en-us/training/paths/get-started-artificial-intelligence/

Deep learning (NVIDIA) -
https://learn.nvidia.com/en-us/training/self-paced-courses

Data science (IBM) -
https://skillsbuild.org/students/course-catalog/data-science

Data Analyst (Microsoft) -
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/

Python (Microsoft) -
https://learn.microsoft.com/en-us/shows/intro-to-python-development/

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Auto-Encoder & Backpropagation by hand ✍️ lecture video ~ 📺 https://byhand.ai/cv/10

It took me a few years to invent this method to show both forward and backward passes for a non-trivial case of a multi-layer perceptron over a batch of inputs, plus gradient descents over multiple epochs, while being able to hand calculate each step and code in Excel at the same time.

= Chapters =
• Encoder & Decoder (00:00)
• Equation (10:09)
• 4-2-4 AutoEncoder (16:38)
• 6-4-2-4-6 AutoEncoder (18:39)
• L2 Loss (20:49)
• L2 Loss Gradient (27:31)
• Backpropagation (30:12)
• Implement Backpropagation (39:00)
• Gradient Descent (44:30)
• Summary (51:39)

#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers

✉️ Our Telegram channels: https://www.tgoop.com/addlist/0f6vfFbEMdAwODBk

This GitHub Repo will be very helpful if you are preparing for a data science technical interview. This question bank covers:

1️⃣ Machine Learning Interview Questions & Answers

2️⃣ Deep Learning Interview Questions & Answers

2.1. Deep learning basics

2.2. Deep learning for computer vision questions

2.3. Deep learning for NLP & LLMs

3️⃣ Probability Interview Questions & Answers

4️⃣ Statistics Interview Questions & Answers

5️⃣ SQL Interview Questions & Answers

6️⃣ Python Questions & Answers

⚡ You can find the repo link in the comments section!