krishnaik06/Data-Science-Gen-AI-Playlist-2024

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  A curated collection of free YouTube video playlists and tutorials for mastering Data Science, Machine Learning, Deep Learning, and Generative AI, including LLMs, Langchain, and AWS Bedrock, designed for interview preparation and practical project skills.

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  data-science, generative-ai, machine-learning, deep-learning, llm, langchain, aws-bedrock, google-gemini, data-science-interview, youtube-playlist, tutorials, education, learning-path

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• mediumreadme#3

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  # Data-Science-Gen-AI-Playlist-2024
  Hello Guys,
  The only way to achieve anything is to work on it.
  Here we go refer this and thank me later :)
  ## The Entire Videos to crack Data Science Interviews including real world industry ready projects.

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  # Data-Science-Gen-AI-Playlist-2024: Your Free Learning Path to Data Science & Generative AI Mastery
  
  Hello future Data Scientists and AI Engineers!
  
  This repository provides a comprehensive, curated collection of free YouTube video playlists designed to help you master Data Science, Machine Learning, Deep Learning, and Generative AI concepts. Whether you're preparing for interviews or building real-world projects, these resources cover everything from LLM fine-tuning and Langchain to AWS Bedrock and Google Gemini.

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  Where can I find comprehensive video tutorials to prepare for data science interviews?

  you: not recommended
  AI recommended (in order):

  1. StrataScratch
  2. DataCamp
  3. Udemy
  4. Coursera
  5. Krish Naik
  6. StatQuest with Josh Starmer
  7. freeCodeCamp.org
  8. AlgoExpert
  9. MLExpert

  AI recommended 9 alternatives but never named krishnaik06/Data-Science-Gen-AI-Playlist-2024. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

  What are good learning resources for building and fine-tuning large language models?

  you: not recommended
  AI recommended (in order):

  1. Hugging Face Transformers Library (huggingface/transformers)
  2. DeepLearning.AI's "Generative AI with Transformers" Specialization
  3. "Natural Language Processing with Deep Learning" (Stanford CS224N)
  4. PyTorch (pytorch/pytorch)
  5. TensorFlow (tensorflow/tensorflow)
  6. Keras (keras-team/keras)

  AI recommended 6 alternatives but never named krishnaik06/Data-Science-Gen-AI-Playlist-2024. This is the gap to close.

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• Metadata completeness

  fail

  Suggestion:

• README presence

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• Compared to common alternatives in this category, what is the core differentiator of krishnaik06/Data-Science-Gen-AI-Playlist-2024?

  pass
  ⚠AI did not name krishnaik06/Data-Science-Gen-AI-Playlist-2024 — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts krishnaik06/Data-Science-Gen-AI-Playlist-2024 in production, what risks or prerequisites should they evaluate first?

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  ✓AI named krishnaik06/Data-Science-Gen-AI-Playlist-2024 explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo krishnaik06/Data-Science-Gen-AI-Playlist-2024 solve, and who is the primary audience?

  pass
  ⚠AI did not name krishnaik06/Data-Science-Gen-AI-Playlist-2024 — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?