ML resource URl's

[sithart]

1. https://www.teachoo.com/4163/768/Example-21---A-man-is-known-to-speak-truth-3-out-of-4-times/category/Examples/
2. https://www.analyticsvidhya.com/blog/2021/04/top-30-mcqs-to-ace-your-data-science-questions-interviews/
3. https://towardsdatascience.com/categorical-feature-encoding-547707acf4e5
4. https://towardsdatascience.com/understanding-na%C3%AFve-bayes-algorithm-f9816f6f74c0
5. https://www.numpyninja.com/post/loss-functions-when-to-use-which-one
6. https://machinelearningmastery.com/how-to-choose-loss-functions-when-training-deep-learning-neural-networks/
7. https://datascience.stackexchange.com/questions/37710/why-does-the-naive-bayes-algorithm-make-the-naive-assumption-that-features-are-i
8. https://datamonje.com/classification-loss-functions/
9. https://datascience.stackexchange.com/questions/57009/why-doesnt-the-binary-classification-log-loss-formula-make-it-explicit-that-nat
10. https://www.nvidia.com/en-us/glossary/data-science/k-means/
11. https://careerfoundry.com/en/blog/data-analytics/what-is-random-forest/
12. https://www.ibm.com/docs/en/ias?topic=knn-usage
13. https://www.scribbr.com/statistics/pearson-correlation-coefficient/

[sithart]

https://devopedia.org/machine-learning

[sithart]

The above one I shared URL, explains the intuitive understanding of Machine learning

[sithart]

Here I attached interesting math blog

• https://mathigon.org/course/sequences/introduction

[sithart]

https://huggingface.co/

[sithart]

https://karpathy.github.io/2021/06/21/blockchain/ https://catenary.wordpress.com/2006/08/19/fun-with-representations-i-nine-numbers/ http://shoefer.github.io/intuitivemi/2015/07/19/data-numbers-representations.html

[sithart]

https://evantoh23.wordpress.com/2011/04/25/car-a-and-car-b-find-the-time-one-overtakes-t/ https://otexts.com/fpp2/moving-averages.html

[sithart]

1, If you want to create the VM on your specified project in GCP, you need to enable the compute engine API on your GCP console.

Path for Enable API: API and services -> Library -> Compute -> Compute Engine API - > Enable API

Creating an Instance in GCP: Login to GCP Console. Choose: Compute Engine -> VM instances -> Create Instance

[sithart]

data science world that 80% of the time spend on a project consists of collecting, cleaning, and organizing data. 20% is where the all the fun happens; models are trained, tested, validated, and then re-trained until the model performance is found to be adequate.

The project included in this blog was the culmination of 2 mon ths of cleaning and organizing data, creating visualizations, a bit of a mental breakdown, and then finally model selection, validation, and tuning. I’ll skip right to parameter tuning to avoid having to re-live through the nightmare of cleaning this dataset.