𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝 𝐬𝐢𝐦𝐩𝐥𝐲

If you’ve just started learning Machine Learning, 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 is one of the most important and misunderstood algorithms.

Here’s everything you need to know 👇

𝟏 ⇨ 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧?

It’s a supervised ML algorithm used to predict probabilities and classify data into binary outcomes (like 0 or 1, Yes or No, Spam or Not Spam).

𝟐 ⇨ 𝐇𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬?

It starts like Linear Regression, but instead of outputting continuous values, it passes the result through a 𝐬𝐢𝐠𝐦𝐨𝐢𝐝 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧 to map the result between 0 and 1.

𝘗𝘳𝘰𝘣𝘢𝘣𝘪𝘭𝘪𝘵𝘺 = 𝟏 / (𝟏 + 𝐞⁻(𝐰𝐱 + 𝐛))

Here,
𝐰 = weights
𝐱 = inputs
𝐛 = bias
𝐞 = Euler’s number (approx. 2.718)

𝟑 ⇨ 𝐖𝐡𝐲 𝐧𝐨𝐭 𝐋𝐢𝐧𝐞𝐚𝐫 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧?

Because Linear Regression predicts any number from -∞ to +∞, which doesn’t make sense for probability.
We need outputs between 0 and 1 and that’s where the sigmoid function helps.

𝟒 ⇨ 𝐋𝐨𝐬𝐬 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐮𝐬𝐞𝐝?

𝐁𝐢𝐧𝐚𝐫𝐲 𝐂𝐫𝐨𝐬𝐬-𝐄𝐧𝐭𝐫𝐨𝐩𝐲

ℒ = −(y log(p) + (1 − y) log(1 − p))
Where y is the actual value (0 or 1), and p is the predicted probability

𝟓 ⇨ 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝐫𝐞𝐚𝐥 𝐥𝐢𝐟𝐞:

𝐄𝐦𝐚𝐢𝐥 𝐒𝐩𝐚𝐦 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧
𝐃𝐢𝐬𝐞𝐚𝐬𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐂𝐡𝐮𝐫𝐧 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐂𝐥𝐢𝐜𝐤-𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐑𝐚𝐭𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐁𝐢𝐧𝐚𝐫𝐲 𝐬𝐞𝐧𝐭𝐢𝐦𝐞𝐧𝐭 𝐜𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧

𝟔 ⇨ 𝐕𝐬. 𝐎𝐭𝐡𝐞𝐫 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫𝐬

It’s fast, interpretable, and easy to implement, but it struggles with non-linearly separable data unlike Decision Trees or SVMs.

𝟕 ⇨ 𝐂𝐚𝐧 𝐢𝐭 𝐡𝐚𝐧𝐝𝐥𝐞 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐜𝐥𝐚𝐬𝐬𝐞𝐬?

Yes, using One-vs-Rest (OvR) or Softmax in Multinomial Logistic Regression.

𝟖 ⇨ 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
pred = model.predict(X_test)

#LogisticRegression #MachineLearning #MLAlgorithms #SupervisedLearning #BinaryClassification #SigmoidFunction #PythonML #ScikitLearn #MLForBeginners #DataScienceBasics #MLExplained #ClassificationModels #AIApplications #PredictiveModeling #MLRoadmap

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LLM cheatsheet ⭐

http://www.tgoop.com/codeprogrammer ✅

Best Data Science Archive Notes

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🐍📰 Linear Algebra in Python: Matrix Inverses and Least Squares — https://realpython.com/python-linear-algebra/

#PythonProgramming #python

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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

Converting Pandas DataFrames to PyTorch DataLoaders for Custom Deep Learning Model Training

Link: https://machinelearningmastery.com/converting-pandas-dataframes-to-pytorch-dataloaders-for-custom-deep-learning-model-training/

#PyTorch #Pandas #DataLoader #DeepLearning #MachineLearning #CustomModelTraining #PythonML #DataPreparation #AIWorkflow #MLPipeline #MachineLearningMastery

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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.

👇👇👇👇👇👇

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Top 50 LLM Interview Questions!

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

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𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝗳𝗼𝗿 𝗠𝗔𝗔𝗡𝗚 & 𝗕𝗲𝘆𝗼𝗻𝗱 🚀
If you're targeting top product companies or leveling up your backend/system design skills, this is for you.

System Design is no longer optional in tech interviews. It’s a must-have.
From Netflix, Amazon, Uber, YouTube, Reddit, Inc., to Twitter, these case studies and topic breakdowns will help you build real-world architectural thinking.

📌 Save this post. Spend 40 mins/day. Stay consistent.


➊ 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗖𝗼𝗿𝗲 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀

👉 System Design Basics
🔗 https://bit.ly/3SuUR0Y)

👉 Horizontal & Vertical Scaling
🔗 https://bit.ly/3slq5xh)

👉 Load Balancing & Message Queues
🔗 https://bit.ly/3sp0FP4)

👉 HLD vs LLD, Hashing, Monolith vs Microservices
🔗 https://bit.ly/3DnEfEm)

👉 Caching, Indexing, Proxies
🔗 https://bit.ly/3SvyVDc)

👉 Networking, CDN, How Browsers Work
🔗 https://bit.ly/3TOHQRb

👉 DB Sharding, CAP Theorem, Schema Design
🔗 https://bit.ly/3CZtfLN

👉 Concurrency, OOP, API Layering
🔗 https://bit.ly/3sqQrhj

👉 Estimation, Performance Optimization
🔗 https://bit.ly/3z9dSPN

👉 MapReduce, Design Patterns
🔗 https://bit.ly/3zcsfmv

👉 SQL vs NoSQL, Cloud Architecture
🔗 https://bit.ly/3z8Aa49)


➋ 𝗠𝗼𝘀𝘁 𝗔𝘀𝗸𝗲𝗱 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀

🔗 https://bit.ly/3Dp40Ux
🔗 https://bit.ly/3E9oH7K


➌ 𝗖𝗮𝘀𝗲 𝗦𝘁𝘂𝗱𝘆 𝗗𝗲𝗲𝗽 𝗗𝗶𝘃𝗲𝘀 (𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗧𝗵𝗲𝘀𝗲!)

👉 Design Netflix
🔗 https://bit.ly/3GrAUG1

👉 Design Reddit
🔗 https://bit.ly/3OgGJrL

👉 Design Messenger
🔗 https://bit.ly/3DoAAXi

👉 Design Instagram
🔗 https://bit.ly/3BFeHlh

👉 Design Dropbox
🔗 https://bit.ly/3SnhncU

👉 Design YouTube
🔗 https://bit.ly/3dFyvvy

👉 Design Tinder
🔗 https://bit.ly/3Mcyj3X

👉 Design Yelp
🔗 https://bit.ly/3E7IgO5

👉 Design WhatsApp
🔗 https://bit.ly/3M2GOhP

👉 Design URL Shortener
🔗 https://bit.ly/3xP078x

👉 Design Amazon Prime Video
🔗https://bit.ly/3hVpWP4

👉 Design Twitter
🔗 https://bit.ly/3qIG9Ih

👉 Design Uber
🔗 https://bit.ly/3fyvnlT

👉 Design TikTok
🔗 https://bit.ly/3UUlKxP

👉 Design Facebook Newsfeed
🔗 https://bit.ly/3RldaW7

👉 Design Web Crawler
🔗 https://bit.ly/3DPZTBB

👉 Design API Rate Limiter
🔗 https://bit.ly/3BIVuh7


➍ 𝗙𝗶𝗻𝗮𝗹 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀

👉 All Solved Case Studies
🔗 https://bit.ly/3dCG1rc

👉 Design Terms & Terminology
🔗 https://bit.ly/3Om9d3H

👉 Complete Basics Series
🔗https://bit.ly/3rG1cfr

#SystemDesign #TechInterviews #MAANGPrep #BackendEngineering #ScalableSystems #HLD #LLD #SoftwareArchitecture #DesignCaseStudies #CloudArchitecture #DataEngineering #DesignPatterns #LoadBalancing #Microservices #DistributedSystems

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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

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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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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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Transformer models have proven highly effective for many NLP tasks. While scaling up with larger dimensions and more layers can increase their power, this also significantly increases computational complexity. Mixture of Experts (MoE) architecture offers an elegant solution by introducing sparsity, allowing models to scale efficiently without proportional computational cost increases.

In this post, you will learn about Mixture of Experts architecture in transformer models. In particular, you will learn about:

Why MoE architecture is needed for efficient transformer scaling
How MoE works and its key components
How to implement MoE in transformer models

Let’s get started:
https://machinelearningmastery.com/mixture-of-experts-architecture-in-transformer-models/

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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

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If you are doing regression modeling in Python for explanatory purposes, don't use scikit-learn - it's not set up for explanatory modeling. Use #statsmodels. It's set up much better for immediately showing you all the underlying parameters of your model and helping you interpret your results..

#analytics #peopleanalytics #datascience #rstats #python

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Unlock the Secrets of #DeepLearning with Math!
Excited to share a free resource for all data science enthusiasts! "Mathematical Theory of Deep Learning" by Philipp Petersen and Jakob Zech is now available on #arXiv.

This book breaks down the core pillars of deep learning with rigorous yet accessible #math. Perfect for grad students, researchers, or anyone curious about why neural networks work so well!

Key Takeaways:
Mastering feedforward neural networks and ReLU's expressive power
Exploring gradient descent, backpropagation, and the loss landscape
Unraveling generalization, double descent, and adversarial robustness.

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