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NOTES ABOUT MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE AI

• Training pending to watch/read:
  • Basic algebra / vectors: https://youtube.com/playlist?list=PL49CF3715CB9EF31D min25 v2

Notes:
Vectors and matrices are basic for machine learning.

• Supervised learning: tagging. http://stanford.io/2nRlxxp
  • Traing with all data, tagging it so it can predict future events. Example: train raspberry pi so it can recognise bird images captured with the camera.
• Semi-supervised learning: reinforcement learning.
  • it does not require training data. But a lot of Try and Error instead.
• Unsupervised learning: Discovering patterns in unlabelled data
  • Is all about clustering data and inferring relationships.
  • k-Means clustering

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• Deep Learning (ie: neuronal networks) http://stanford.io/2BsQ91Q
  • Layers: Input, Hidden, Output. But also Bias input (poking the hidden layers)

• Reinforcement Learning: BEYOND SELF SUPERVISION TODO

• Train the model but also transfer learning: reuse existing models.

• Tips and tricks: http://stanford.io/2MEHwFM

• For model complexity
  • low: bias (flat line(
  • high: a lot of variance (adjust data a lot, not good either

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• BOOK Oreilly: 'Applied Machine Learning and AI for Engineers' Jeff Proise github « book
  • /Documents/PLURALSIGHT/datascience/Applied-Machine-Learning-main
  • source /Users/santosj/Documents/PLURALSIGHT/datascience/bin/activate
  • jupyter notebook /Users/santosj/Documents/PLURALSIGHT/datascience/Applied-Machine-Learning-main
• (buy the book later on): https://github.com/javier-antich/ml4nce/blob/main/UC2/UC2-multivariate-outlier-detection.ipynb

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• Future reading: Machine Learning for Network and Cloud Engineers External Link
• Oreilly: Machine Learning with scikit-learn David Mertz github
  • http://localhost:8888/notebooks/WhatIsML.ipynb
• Oreilly: Wide overview by Rob Barton, Jerome Henry : ml_fundamentals.pfd.pdf Rob Barton, Jerome « DONE
• Oreilly: models and misfits by Dr Mark Fenner : ml_models_misfits_drmarkfenner.pdf ; https://github.com/mfenner1/mlwpy_live « DONE

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• Managed datasets with panda's and scikit-learn
  • Link

• convolution studies how a shape is modified by another)
• cnn relu cnn relu cnn …