TODO - TOPICS.txt

SQL
Query optimization (both for SQL and noSQL systems).


NAN Handling
Class Imbalance
https://towardsdatascience.com/fraud-detection-under-extreme-class-imbalance-c241854e60c

Dimensionality Reduction
* PCA

Feature Selection

  Filter Methods
  Wrapper Methods
  Embedded Methods
    Lasso:
    Ridge:

Ensemble
https://towardsdatascience.com/study-of-decision-trees-and-ensembles-on-scikit-learn-e713a8e532b8
Models
  Baseline model using logistic regression and linear regression

Decision Trees
Difference btween supervised and un supervised learning

Information Gain
  - Gini
  - Entropy

Clustering
  - Elbow method

Model Evaluation
Classification Score
  Learning Curve
  Bias Variance Trade off
  https://cdn-images-1.medium.com/max/800/1*Uzjz7fIFjPMQp0nCXp8QiQ.png


ROC
  The ROC curve can also help debug a model. For example, if the bottom left corner of the curve is closer to the random line, it implies that the model is misclassifying at Y=0. Whereas, if it is random on the top right, it implies the errors are occurring at Y=1. Also, if there are spikes on the curve (as opposed to being smooth), it implies the model is not stable. When dealing with fraud models, ROC is your best friend.
  https://www.kdnuggets.com/2018/07/receiver-operating-characteristic-curves-demystified-python.html



#### Difference between clustering and classification

  *Classification*- A data-set can have different groups/ classes. red, green and black. Classification will try to find rules that divides them in different classes.
  *Custering*- if a data-set is not having any class and you want to put them in some class/grouping, you do clustering. The purple circles above.

  One liner for Classification:

  Classifying data into pre-defined categories

  One liner for Clustering:

  Grouping data into a set of categories

  Key difference:

  Classification is taking data and putting it into pre-defined categories and in Clustering the set of categories, that you want to group the data into, is not known beforehand.

  Conclusion:

  Classification assigns the category to 1 new item, based on already labeled items while Clustering takes a bunch of unlabeled items and divide them into the categories
  In Classification, the categories\groups to be divided are known beforehand while in Clustering, the categories\groups to be divided are unknown beforehand
  In Classification, there are 2 phases – Training phase and then the test phase while in Clustering, there is only 1 phase – dividing of training data in clusters
  Classification is Supervised Learning while Clustering is Unsupervised Learning

https://neelbhatt40.wordpress.com/2017/11/21/classification-and-clustering-machine-learning-interview-questions-answers-part-i/
http://www.differencebetween.net/technology/difference-between-clustering-and-classification/




https://www.kdnuggets.com/2018/07/beginners-ask-how-many-hidden-layers-neurons-neural-networks.html
https://www.kdnuggets.com/2018/07/devops-data-scientists-taming-unicorn.html
https://www.kdnuggets.com/2017/06/feature-engineering-help-kaggle-competition-1.html
https://medium.com/unstructured/how-feature-engineering-can-help-you-do-well-in-a-kaggle-competition-part-ii-3645d92282b8
https://www.kdnuggets.com/2018/04/operational-machine-learning-successful-mlops.html



Data Science Portfolio
https://whatsthebigdata.com/2015/10/17/how-to-become-a-unicorn-data-scientist-and-make-more-than-240000/
https://towardsdatascience.com/how-to-build-a-data-science-portfolio-5f566517c79c
https://reshamas.github.io/first-data-science-job/
https://towardsdatascience.com/how-to-get-a-job-as-a-data-scientist-f417078fe13e
https://whatsthebigdata.com/2015/10/17/how-to-become-a-unicorn-data-scientist-and-make-more-than-240000/
https://reshamas.github.io/
https://www.kdnuggets.com/2018/07/build-data-science-portfolio.html