100+ LLM Interview Questions and Answers (GitHub Repo)

Anyone preparing for #AI/#ML Interviews, it is mandatory to have good knowledge related to #LLM topics.

This# repo includes 100+ LLM interview questions (with answers) spanning over LLM topics like
LLM Inference
LLM Fine-Tuning
LLM Architectures
LLM Pretraining
Prompt Engineering
etc.

🖕 Github Repo - https://github.com/KalyanKS-NLP/LLM-Interview-Questions-and-Answers-Hub

https://t.me/DataScienceM ✅

🚀 Top 9 Predictive Models Every Data Scientist Should Know in 2025

In the world of Machine Learning, selecting the right predictive model is crucial for solving real-world problems effectively.

Here’s a deep dive into the top 9 models and when to use them :-

1️⃣ Regularized Linear/Logistic Regression

• Best for: Tabular data with mostly linear effects
• Why: Fast, interpretable, strong baseline
• Watch out: Multicollinearity, feature scaling
• Key knobs: L1/L2/Elastic Net strength

2️⃣ Decision Trees

• Best for: Simple rules and quick interpretability
• Why: Captures nonlinearity and feature interactions
• Watch out: Overfitting
• Key knobs: max_depth, min_samples_leaf

3️⃣ Random Forest

• Best for: Mixed-type tabular data
• Why: Robust, handles missingness, low tuning effort
• Watch out: Slower inference for large models
• Key knobs: n_estimators, max_features

4️⃣ Gradient Boosting Trees

• Best for: Structured data requiring top performance
• Why: Handles complex patterns and interactions
• Watch out: Overfitting if not tuned carefully
• Key knobs: learning_rate, n_estimators, max_depth, regularization

5️⃣ Support Vector Machines (linear/RBF)

• Best for: Medium-sized datasets with clear margins
• Why: Strong performance after scaling
• Watch out: Kernel choice and cost at scale
• Key knobs: C, kernel, gamma

6️⃣ k-Nearest Neighbors (k-NN)

• Best for: Small datasets with local structure
• Why: Simple, non-parametric
• Watch out: Poor scaling, sensitive to feature scaling
• Key knobs: k, distance metric, weighting

7️⃣ Naive Bayes

• Best for: High-dimensional sparse features (like text)
• Why: Very fast, competitive for many applications
• Watch out: Independence assumption
• Key knobs: smoothing (alpha)

8️⃣ Multilayer Perceptrons (Feedforward Neural Networks)

• Best for: Nonlinear relationships with sufficient data & compute
• Why: Flexible universal approximators
• Watch out: Tuning, overfitting without regularization
• Key knobs: layers/neurons, dropout, learning rate

9️⃣ Classical Time-Series Models

• Best for: Univariate or small-multivariate forecasting with seasonality
• Why: Transparent baselines, good for limited data
• Watch out: Stationarity, careful feature engineering
• Key knobs: (p, d, q), seasonal terms, exogenous variables

💡 Pro Tip: Each model has its strengths and trade-offs. Understanding when to use which model and how to tune its hyperparameters is key to building robust and interpretable predictive systems.

https://t.me/DataScienceM ✅

🚀Stanford just completed a must-watch for anyone serious about AI:

🎓 “𝗖𝗠𝗘 𝟮𝟵𝟱: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 & 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀” is now live entirely on YouTube and it’s pure gold.

If you’re building your AI career, stop scrolling.
This isn’t another surface-level overview. It’s the clearest, most structured intro to LLMs you could follow, straight from the Stanford Autumn 2025 curriculum.

📚 𝗧𝗼𝗽𝗶𝗰𝘀 𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗶𝗻𝗰𝗹𝘂𝗱𝗲:
• How Transformers actually work (tokenization, attention, embeddings)
• Decoding strategies & MoEs
• LLM finetuning (LoRA, RLHF, supervised)
• Evaluation techniques (LLM-as-a-judge)
• Optimization tricks (RoPE, quantization, approximations)
• Reasoning & scaling
• Agentic workflows (RAG, tool calling)

🧠 My workflow: I usually take the transcripts, feed them into NotebookLM, and once I’ve done the lectures, I replay them during walks or commutes. That combo works wonders for retention.

🎥 Watch these now:

- Lecture 1: https://lnkd.in/dDER-qyp
- Lecture 2: https://lnkd.in/dk-tGUDm
- Lecture 3: https://lnkd.in/drAPdjJY
- Lecture 4: https://lnkd.in/e_RSgMz7
- Lecture 5: https://lnkd.in/eivMA9pe
- Lecture 6: https://lnkd.in/eYwwwMXn
- Lecture 7: https://lnkd.in/eKwkEDXV
- Lecture 8: https://lnkd.in/eEWvyfyK
- Lecture 9: https://lnkd.in/euiKRGaQ

🗓 Do yourself a favor for this 2026: block 2-3 hours per week / llectue and go through them.

If you’re in AI — whether building infra, agents, or apps — this is the foundational course you don’t want to miss.

Let’s level up.
https://t.me/CodeProgrammer 😅

🚀 Master Data Science & Programming!

Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!


🔰 Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.me/CodeProgrammer

🔖 Machine Learning
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://t.me/DataScienceM

🧠 Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4

🎯 PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ

💾 Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1

🧑‍🎓 Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC

😀 ML Research Hub
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT

💬 Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9

🐍 Python Arab| بايثون عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab

🖊 Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.me/DataScienceN

📺 Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV

📈 Data Analytics
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX

🎧 Learn Python Hub
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://t.me/Python53

⭐️ Research Papers
Professional Academic Writing & Simulation Services
https://t.me/DataScienceY

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Admin: @HusseinSheikho

AI-ML Roadmap from Scratch

👉 https://github.com/aadi1011/AI-ML-Roadmap-from-scratch?tab=readme-ov-file

https://t.me/CodeProgrammer 🌟

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