---

-   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](https://christophm.github.io/interpretable-ml-book/)

---

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 {#questions}

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


## Supervised {#supervised}

-   [Lecture 1: Supervised Learning](http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote01%5FMLsetup.html)


### [regression]({{<relref "../../Documents/org-notes/2021-05-26--11-12-25Z--regression.md#" >}}) {#regression--dot-dot-dot-dot-documents-org-notes-2021-05-26-11-12-25z-regression-dot-md}


### classfication {#classfication}


## Unsupervised {#unsupervised}


### [clustering]({{<relref "../../Documents/org-notes/2021-05-26--11-13-54Z--clustering.md#" >}}) {#clustering--dot-dot-dot-dot-documents-org-notes-2021-05-26-11-13-54z-clustering-dot-md}


### [dimensionality reduction]({{<relref "../../Documents/org-notes/2021-05-26--11-16-28Z--dimensionality_reduction.md#" >}}) {#dimensionality-reduction--dot-dot-dot-dot-documents-org-notes-2021-05-26-11-16-28z-dimensionality-reduction-dot-md}


## [ML Model Evaluation]({{<relref "../../Documents/org-notes/20210224162319-model_evaluations_score.md#" >}}) {#ml-model-evaluation--dot-dot-dot-dot-documents-org-notes-20210224162319-model-evaluations-score-dot-md}


## Graph Based {#graph-based}


## Resources {#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 {#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>