Comprehensive-Data-Science-Notes

KO | EN

gitlite — search

#react #ai #typescript #llm #golang #go #rust #docker #java #cli #kubernetes #vue #security #pytorch

Comprehensive-Data-Science-Notes

(★ 9)

Data Science Notes collected from Various Sources

Comprehensive-Data-Science-Notes Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

## COMPREHENSIVE DATA SCIENCE NOTES
## HAPPY LEARNING ⊂◉‿◉つ
## It contains Data Science Notes collaborated from various Data Science Experts. Happy Learning !!

💡 Important Concepts:

1. What is the difference between filter, wrapper, and embedded methods for feature selection? [Answer](https://sebastianraschka.com/faq/docs/feature_sele_categories.html)
2. 120 Questions. [Answer](https://towardsdatascience.com/120-data-scientist-interview-questions-and-answers-you-should-know-in-2021-b2faf7de8f3e)
3. Probability vs. Likelihood. [Answer](https://stats.stackexchange.com/questions/2641/what-is-the-difference-between-likelihood-and-probability#2647)
   My Fav.: [StatQuest](https://www.youtube.com/watch?v=pYxNSUDSFH4)
4. Generative and discriminative. [Answer](https://stackoverflow.com/questions/879432/what-is-the-difference-between-a-generative-and-a-discriminative-algorithm)
5. ML concepts and code. [Answer](https://ml-cheatsheet.readthedocs.io/en/latest/linear_regression.html)
6. EM - Expectation-Maximization. [Answer](Expectation-Maximization)
7. Random Forest. [Answer](https://www.youtube.com/watch?v=J4Wdy0Wc_xQ) 
8. Regression - Type of change. [Answer](https://web.stanford.edu/~mrosenfe/soc_meth_proj3/soc_180B_regression_whatchanges.htm)
9. Pearson vs Spearman vs Kendall: [Stackexchange](https://datascience.stackexchange.com/questions/64260/pearson-vs-spearman-vs-kendall)
10. Gain and Lift Charts. [listendata](https://www.listendata.com/2014/08/excel-template-gain-and-lift-charts.html)
11. Statistical Hypothesis tests in Python. [Jason](https://machinelearningmastery.com/statistical-hypothesis-tests-in-python-cheat-sheet/)
12. Machine learning system design. [Link](https://huyenchip.com/machine-learning-systems-design/toc.html)
13. A/B Testing. [Link](https://nancyyanyu.github.io/posts/17c5bb19/), [Link](https://www.youtube.com/watch?v=DUNk4GPZ9bw&ab_channel=DataInterview)
14. Product Questions. [Quora](https://www.quora.com/profile/Teng-Lu-1/answers)
15. Random Forest to Layman. [Quora](https://www.quora.com/How-does-randomization-in-a-random-forest-work)
16. ANOVA, ANCOVA etc. [Link](http://www.statsmakemecry.com/smmctheblog/stats-soup-anova-ancova-manova-mancova)
17. ML System Design Template [Link](https://www.mle-interviews.com/ml-design-template)

## Useful blogs to refer:

1. Martin Henze (Heads or Tails). [Blog](https://heads0rtai1s.github.io/2020/11/05/r-python-dplyr-pandas/)
2. Python Snippets. [Link](https://github.com/dushyantkhosla/python-snippets)
3. PandasVault. [Link](https://github.com/firmai/pandasvault#shift-columns-to-front)
4. Python Engineer. [Twitter](https://twitter.com/python_engineer)
5. Paired vs Unpaired data: [link](https://socratic.org/questions/what-is-a-paired-and-unpaired-t-test-what-are-the-differences)
6. Data informed product building: [Link](https://medium.com/sequoia-capital/data-informed-product-building-1e509a5c4112)
7. Metric: [Link](https://productlessons.substack.com/p/what-to-do-when-your-metrics-dip), [Link](https://igotanoffer.com/blogs/product-manager/product-metric-interview-questions),[SQL](https://quip.com/2gwZArKuWk7W)
8. Into to Linear Algebra: [Link](https://pabloinsente.github.io/intro-linear-algebra)
9. IMS data sources: [Link](https://csimarket.com/stocks/segments.php?code=RX)
10. Predictive model performance check: [ListenData](https://www.listendata.com/2015/01/model-performance-in-logistic-regression.html)
11. Case Study: [Link](https://hackingthecaseinterview.thinkific.com/pages/market-entry-case-interview)
12.  Collection of cases: [Link](https://hackingthecaseinterview.thinkific.com/pages/articles), [GAME](https://hackernoon.com/metrics-game-framework-5e3dce1be8ac)
13.  Gradient Boosting: [Link](https://www.youtube.com/watch?v=3CC4N4z3GJc&ab_channel=StatQuestwithJoshStarmer)
14.  Federated learning: [Link](https://www.quora.com/What-is-federated-learning), [Link2](https://federated.withgoogle.com/)
15.  MLOps: [Link](https://github.com/GokuMohandas/madewithml)
16.  Mixed Effect Models: [Link](https://towardsdatascience.com/how-linear-mixed-model-works-350950a82911), [Link1](https://medium.com/analytics-vidhya/introduction-to-mixed-models-208f012aa865)
17.  ML System feature store: [Link](https://medium.com/data-for-ai/comprehensive-and-comparative-list-of-feature-store-architectures-for-data-scientists-and-big-data-86ea8c4d853b)
18.  Data Science Cheat Sheet: [Link](https://www.theinsaneapp.com/2020/12/machine-learning-and-data-science-cheat-sheets-pdf.html)
19.  Things can go wrong: [Link](https://towardsdatascience.com/51-things-that-can-go-wrong-in-a-real-world-ml-project-c36678065a75)
20.  Transformers from scratch [Link](https://e2eml.school/transformers.html)
21.  Dive into Deep Learning [Link](https://d2l.ai/chapter_preface/index.html)
22.  DL Interview [Link](https://arxiv.org/ftp/arxiv/papers/2201/2201.00650.pdf)
23.  DL Rules of Thumb [Link](https://jeffmacaluso.github.io/post/DeepLearningRulesOfThumb/)
24.  ML Forecasting [Link](https://towardsdatascience.com/ml-time-series-forecasting-the-right-way-cbf3678845ff)
25.  MLOps without much Ops [Link](https://towardsdatascience.com/mlops-without-much-ops-d17f502f76e8)
26.  Rules of Machine Learning by Google [Link](https://developers.google.com/machine-learning/guides/rules-of-ml)
27.  Product Management for AI [Link](https://www.oreilly.com/radar/product-management-for-ai/)

## ML System Design:

1. Framework [Link](https://leetcode.com/discuss/interview-question/system-design/566057/machine-learning-system-design-a-framework-for-the-interview-day)
2. Product minded ML design. [Link](https://www.youtube.com/watch?v=Hv54e-9XnZ0&ab_channel=AssociationforComputingMachinery%28ACM%29)
3. ML Design [Link](https://github.com/khangich/machine-learning-interview/blob/master/design.md)
4. MLE Book [Link](http://www.mlebook.com/wiki/doku.php)
5. ML System design [Link](https://becominghuman.ai/machine-learning-system-design-f2f4018f2f8)
6. Full stack deep learning [Link](https://fall2019.fullstackdeeplearning.com/)
7. Production Machine Learning Problems [Link](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/46178.pdf)
8. ML System Design Resources [Link](https://www.teamblind.com/post/Machine-learning-engineering-and-ML-systems-design-resources-master-list-gWY7ZUTT)
9. Metric Question [Link](https://medium.com/datainterview/principles-and-frameworks-of-product-metrics-youtube-case-study-ff63257a82d3)
10. Product Matrics [Link](https://medium.com/datainterview/principles-and-frameworks-of-product-metrics-youtube-case-study-ff63257a82d3)
11. ML Stack Template [Link](https://ml-ops.org/content/state-of-mlops)
12. Patrick Halina - ML Design [Link](http://patrickhalina.com/posts/ml-systems-design-interview-guide/)
13. ML Interview [Link](https://github.com/alirezadir/machine-learning-interview-enlightener)
14. ML Cheat Sheet [Link](https://sites.google.com/view/datascience-cheat-sheets/machine-learning_1)
15. ML Project Timelines [Link](https://docs.google.com/document/d/1D-M6nxeLnIaFufS-u2Ymp45AYB9eEmVHZno7G2F545U/edit)

## Useful LinkedIn Posts:

Understand the business context first, don't get over-excited about the tech, and jump into coding too early.

When someone asks you for a model, always ask:

👉 why do you need it?

👉 what is your current solution (e.g. what is the baseline to beat)?

👉 who is going to use the predictions and how?

👉 what is the financial impact of the model’s downtime or mistakes?

👉 which metrics do we care about to measure what?

Once you have your answers, back them up with a solid exploratory data analysis, and, when done, loop in the biz team again.

This is a critical moment as your results will translate into 3 potential outcomes:

💡 “Really? This contradicts what I thought. Well, in this case, the ML model doesn’t make much sense anymore”. You are off the hook without a single line of code 🔴

💡 “Ah, interesting. I guess we’ll have to change requirements/scope then.” Course-correct before moving forward 🟠

💡 “This is what I expected. Let’s go ahead”. Green light 🟢

Comprehensive-Data-Science-Notes

Comprehensive-Data-Science-Notes Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Comprehensive-Data-Science-Notes

Comprehensive-Data-Science-Notes Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README