interviews.ai
machine_learning_refined
| interviews.ai | machine_learning_refined | |
|---|---|---|
| 12 | 3 | |
| 4,437 | 1,588 | |
| - | - | |
| 0.0 | 6.6 | |
| over 2 years ago | 10 months ago | |
| Python | ||
| - | GNU General Public License v3.0 or later |
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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
machine_learning_refined
- Machine Learning Refined
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Hands on ML + Introduction to Statistical Learning?
Perceptron from Scratch
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Taking CS 349 (Machine Learning) in Fall
here's the repo that CS375/475 uses: https://github.com/jermwatt/machine_learning_refined.
What are some alternatives?
machine-learning-roadmap - A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.
ivy - The Unified AI Framework
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Awesome-pytorch-list - A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
python - 🚀 Curated collection of Amazing Python scripts from Basics to Advance with automation task scripts using Libraries and Logic. These things everyone should know in their journey with programming.
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.
ivy - The Unified Machine Learning Framework [Moved to: https://github.com/unifyai/ivy]
pennylane - PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.
gurobi-machinelearning - Formulate trained predictors in Gurobi models
Diabetes-Prediction-Using-SVM - In this case, we train our model with several medical informations such as the blood glucose level, insulin level of patients along with whether the person has diabetes or not so this act as labels whether that person is diabetic or non-diabetic so this will be label for this case.
numpy-string-indexed - NumPy extension that allows arrays to be indexed using labels