machine learning

• references
  • Reading
    • https://www.cs.cmu.edu/~tom/mlbook.html
  • To Read
    • https://rentruewang.github.io/learning-machine/intro.html
    • https://medium.com/octavian-ai/how-to-get-started-with-machine-learning-on-graphs-7f0795c83763
    • https://medium.datadriveninvestor.com/3-steps-introduction-to-machine-learning-and-design-of-a-learning-system-bd12b65aa50c
    • https://chrisalbon.com/
    • Interpretable Machine Learning

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.

Machine learning is a subfield of AI that studies the ability to improve performance based on experience.Some AI systems use machine learning methods to achieve competence, but some do not.

• A checkers learning problem:
  • Task T: playing checkers
  • Performance measure P: percent of games won against opponents
  • Training experience E: playing practice games against itself

Questions

• If model deployed in the prod env is broken, what would be your approach to debug it?

Supervised

• Lecture 1: Supervised Learning

regression

classfication

Unsupervised

clustering

dimensionality reduction

ML Model Evaluation

Graph Based

Resources

• Pandas Guide , axis = https://stackoverflow.com/a/49884677/8240555
• Numpy Guide, https://scipy-lectures.org/
• https://gettingstarted.ml
• https://github.com/brylevkirill/notes
• https://github.com/ujjwalkarn/Machine-Learning-Tutorials [10.6k stars]
• Computer Vision
  • https://github.com/kuzand/Computer-Vision-Video-Lectures
• Visualization
  • https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots-python/
• Optimization
  • https://cs.uwaterloo.ca/~y328yu/mycourses/794-2020/lecture.html

Useful Links

• https://datascience.stackexchange.com/questions/5277/do-you-have-to-normalize-data-when-building-decision-trees-using-r
• https://stats.stackexchange.com/questions/48267/mean-absolute-error-or-root-mean-squared-error
• https://datascience.stackexchange.com/questions/36945/interpreting-the-root-mean-squared-error-rmse
• https://www.dataquest.io/blog/understanding-regression-error-metrics/
• https://machinelearningmastery.com/metrics-evaluate-machine-learning-algorithms-python/
• https://stackoverflow.com/questions/8961586/do-i-need-to-normalize-or-scale-data-for-randomforest-r-package
• https://towardsdatascience.com/hyperparameter-tuning-the-random-forest-in-python-using-scikit-learn-28d2aa77dd74
• https://medium.com/@cjl2fv/an-intro-to-hyper-parameter-optimization-using-grid-search-and-random-search-d73b9834ca0a