Machine Learning Survival Guide (How I Got Through Intro to Machine Learning At University)
Channel: Mark Bacon
Date: 2020-10-29

Getting stuck on some Machine Learning theory or just don't know where to go? Here's some advice from my struggle through the class last spring!Thanks to Primal Power for leaving a comment for this video topic. If you have an idea, drop it below!
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📃 Referenced Resources
Python: https://www.python.org/
NumPy: https://numpy.org/
Pandas: https://pandas.pydata.org/
Scikit Learn: https://scikit-learn.org/stable/
Coursera: coursera.org/
John Wittenauer: https://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/
Derek Banas Python: https://www.youtube.com/watch?v=N4mEzFDjqtA
Elite Data Science: https://elitedatascience.com/learn-machine-learning
3Blue1Brown ML: https://www.youtube.com/watch?v=aircAruvnKk
Neural Networks (short book): http://neuralnetworksanddeeplearning.com/
Kaggle: https://www.kaggle.com/
UCI ML Repository: https://archive.ics.uci.edu/ml/index.php
StatQuest: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw

👨‍💻 About Me - Instagram: @markbacon78
I record, edit, and upload a wide variety of videos of how my weeks go, various events happening alongside my projects planning and creation processes. I am a student at NYU studing Computer Science, Psychology and Linguistics, and minoring in Game Design, so I'm happy to answer any and all questions either in the comments or by email (in the "About" section). From animation to NYU, ask away! I look forward to seeing you in future videos.

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⏰ Timestamps
00:27 - Introduction
01:51 - Things You'll Need to Know
06:13 - Python Resources and Libraries
09:58 - External Resources
12:39 - Ways to Apply What You Learn

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