machine-learning-roadmap
interviews.ai
| machine-learning-roadmap | interviews.ai | |
|---|---|---|
| 5 | 12 | |
| 7,164 | 4,437 | |
| - | - | |
| 0.0 | 0.0 | |
| over 1 year ago | over 2 years ago | |
| MIT License | - |
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machine-learning-roadmap
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Best AI ML DL DS Roadmap
**[Mrdbourke/machine-learning-roadmap on GitHub](https://github.com/mrdbourke/machine-learning-roadmap)**: This GitHub repository is more focused on machine learning. It's a good choice if you're looking for a more community-driven approach, as GitHub repositories often encourage contributions and updates from various experts.
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[D] Best AI ML DL DS Roadmap
Some roadmaps I have found: - [roadmap.sh] AI and Data Scientist Roadmap ← Best? - [i.am.ai] AI Expert Roadmap - [github.com] mrdbourke/machine-learning-roadmap - [github.com] luspr/awesome-ml-courses - [rentry.org] Machine Learning Roadmap
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100+ Must Know Github Repositories For Any Programmer
7. Machine Learning Roadmap
- Where can I start?
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Machine Learning Roadmap
Original article here: https://github.com/mrdbourke/machine-learning-roadmap
interviews.ai
- Deep Learning Interviews
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Ask HN: Leet code/CTCI equivalent for Data science/ML roles
scientists" - those interviews focus a lot of SQL, product metrics, A/B testing etc. You can also do SQL problems on leetcode for those types of positions.
2. Deep learning interviews book for ML positions - https://github.com/BoltzmannEntropy/interviews.ai - it's a bit too deep and advanced for most interviews though so don't be intimidated if you can't cover everything. Don't read this book if you're applying for a product DS position (and vice versa). You can also replace this with an ML theory book of your choice if you like.
3. Still leetcode and CTCI because they often come up for ML positions anyway.
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what to study for MLE interviews? Is it leetcode all the way?
Regarding how to study, my suggestion is to solve problems with sample datasets. A couple of books that might come in handy. 1. https://github.com/BoltzmannEntropy/interviews.ai - I like this because there are problems and solutions in there. 2. https://huyenchip.com/ml-interviews-book/
- Deep Learning Interviews book: Hundreds of fully solved job interview questions from a wide range of key topics in AI.
- GitHub - BoltzmannEntropy/interviews.ai: Deep Learning Interviews book: Hundreds of fully solved job interview questions from a wide range of key topics in AI
- Deep Learning Interviews: Hundreds of fully solved job interview questions from a wide range of key topics in AI
- Deep Learning Interviews book: Hundreds of fully solved job interview questions
What are some alternatives?
applied-ml - 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
audio-ai-timeline - A timeline of the latest AI models for audio generation, starting in 2023!
stanford-cs-229-machine-learning - VIP cheatsheets for Stanford's CS 229 Machine Learning
Awesome-pytorch-list - A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
yt-channels-DS-AI-ML-CS - A comprehensive list of 180+ YouTube Channels for Data Science, Data Engineering, Machine Learning, Deep learning, Computer Science, programming, software engineering, etc.
high-school-guide-to-machine-learning - Being a high schooler myself and having studied Machine Learning and Artificial Intelligence for a year now, I believe that there fails to exist a learning path in this field for High School students. This is my attempt at creating one.
Hello-Kaggle - For someone who is new at Kaggle
Knet.jl - Koç University deep learning framework.
awesome-datascience - :memo: An awesome Data Science repository to learn and apply for real world problems.
nodebestpractices - :white_check_mark: The Node.js best practices list (February 2024)
You-Dont-Know-JS - A book series on JavaScript. @YDKJS on twitter.
react-boilerplate - :fire: A highly scalable, offline-first foundation with the best developer experience and a focus on performance and best practices.