10 YouTube videos for anyone starting with machine learning.

On libraries, algorithms, and tools:

1. Notebooks are a fantastic way to code, experiment, and communicate your results. Take a look at Corey Schafer fantastic 30-minute tutorial on Jupyter Notebooks. https://m.youtube.com/watch?v=HW29067qVWk


2. The Pandas library is the gold standard to manipulate structured data. Check out Joe James "Pandas Tutorial. Intro to DataFrames."
https://m.youtube.com/watch?v=HW29067qVWk

3. Data visualization is key for anyone practicing machine learning.
Check out Blondiebytes's "Learn Matplotlib in 6 minutes" tutorial.
https://youtu.be/nzKy9GY12yo

4. Another trendy data visualization library is Seaborn. Derek Banas put together "Seaborn Tutorial 2020," which is highly recommend. https://youtu.be/6GUZXDef2U0

5. Numpy is another Python library that you will use every single day.
Keith Galli's "Complete Python NumPy Tutorial" is a great start. https://youtu.be/6GUZXDef2U0

6. One of the most basic algorithms that you can learn is Decision Trees.
Watch Josh Gordon's video where he builds a decision tree from scratch. https://youtu.be/LDRbO9a6XPU

7. It's hard to talk about the Machine learning without touching on Neural Networks. Probably the best video out there that explains how neural networks work is 3blue1brown's: https://m.youtube.com/watch?v=aircAruvnKk

8. Scikit-Learn is one of the most popular machine learning libraries out there. Simplilearn's "Scikit-Learn Tutorial" is a great place to start. https://youtu.be/0Lt9w-BxKFQ

9. TensorFlow is the most popular deep learning library that's currently used in the industry. Here is a massive 7-hour tutorial of TensorFlow 2.0 produced by Free Code Camp. https://youtu.be/tPYj3fFJGjk

10. Finally, a great way to start getting familiar with machine learning is the bite-sized recipes published by Google. This series is worth every minute. Playlist: https://m.youtube.com/watch?v=cKxRvEZd3Mw&list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal

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CS 324 - Advances in Foundation Models

https://stanford-cs324.github.io/winter2023/syllabus/

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✅ Model-Based Reinforcement Learning: From Data to Continuous Actions with a Python-based Toolbox


❇️ Explore a comprehensive and practical approach to reinforcement learning.

❇️ Reinforcement learning is an essential paradigm of machine learning, wherein an intelligent agent performs actions that ensure optimal behavior from devices. While this paradigm of machine learning has gained tremendous success and popularity in recent years, previous scholarship has focused either on theory―optimal control and dynamic programming – or on algorithms―most of which are simulation-based.

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