Data science/ML/AI

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Data Science and Machine Learning [PDF] Mathematical and Statistical Methods Dirk P. Kroese, Zdravko I. Botev, Thomas Taimre, Radislav Vaisman 8th May 2022 533 pages 🔗 Read online

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Hey folks, this week's round of our programming quiz league is about data science. Here is the quiz link: http://t.me/QuizBot?start=H4Ow9sU8 Feel free to answer on those 8 short questions and let me know about your placement on final score. Also to those who celebrate today I wish Merry Christmas 🎄🥳😊

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Often the hardest part of solving a machine learning problem can be finding the right estimator for the job. Different estimators are better suited for different types of data and different problems. The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data. Source: Scikit-learn

Graphic SQL reference for data manipulation Source: https://raw.githubusercontent.com/kelsfarmer/SQL/master/SQL%20Data%20Manipulation%20Language%20Cheat%20Sheet.png

DIMENSIONALITY REDUCTION Have you heard of Dimensionality Reduction👀? If this is your first time😃, then get your seats closer🙂. It means trimming down data to remove unwanted features👌. Did this make any sense🤷‍♀️? If it didn't then you must know that whenever you have a very large dataset, It can help you capture the majority of your dataset's information within a few number of features. Here's one method😃 of Dimensionality Reduction you must know. It's the Principal Component Analysis (PCA)😎. It gives us the ability to plot multivariate data🤯 in 2 dimensions and works perfectly☺️ in identifying the axis of greatest variance in our dataset. In this method, we take old sets of variables and convert them into a newer set. The new sets created are called principal components⭐️. There is a trade-off between reducing the number of variables while maintaining the accuracy of your model👍🏼. The next time you have problems working with very large datasets 🤯, you could try Dimensionality Reduction😉