Post Below are the useful links for Regression:

Below are the useful links for Regression: https://www.seldon.io/machine-learning-regression-explained 🫱Linear Regression https://towardsdatascience.com/mathematics-hidden-behind-linear-regression-431fe4d11969 https://christophm.github.io/interpretable-ml-book/limo.html https://www.youtube.com/watch?v=PaFPbb66DxQ https://www.youtube.com/watch?v=3dhcmeOTZ_Q&t=35s https://christophm.github.io/interpretable-ml-book/limo.html 🫱Gradient Descent: https://youtu.be/qg4PchTECck?si=kau7fBI5Lgb0kGo1 https://youtu.be/sDv4f4s2SB8?si=WxA38LHuXQf13nF- https://d2l.ai/chapter_optimization/gd.html https://machinelearningmastery.com/gradient-descent-for-machine-learning/ https://www.ruder.io/optimizing-gradient-descent/ 🫱Polynomial Regression https://www.analyticsvidhya.com/blog/2021/07/all-you-need-to-know-about-polynomial-regression/ https://www.youtube.com/watch?v=QptI-vDle8Y 🫱KNN https://youtu.be/sTJApaBjong?si=gycuLY3cCYXMDnH4 🫱About metrics: https://machinelearningmastery.com/regression-metrics-for-machine-learning/ https://www.youtube.com/watch?v=2AQKmw14mHM&ab_channel=StatQuestwithJoshStarmer https://thestatsgeek.com/2013/10/28/r-squared-and-adjusted-r-squared/ https://stattrek.com/regression/residual-analysis https://medium.com/@HalderNilimesh/understanding-residual-plots-in-linear-regression-models-a-comprehensive-guide-with-examples-2fb5a60daf26 https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_percentage_error.html https://en.wikipedia.org/wiki/Mean_absolute_percentage_error 🫱Loss Function https://www.analyticsvidhya.com/blog/2019/08/detailed-guide-7-loss-functions-machine-learning-python-code/#Difference_Between_Loss_Function_and_Cost_Function https://heartbeat.comet.ml/5-regression-loss-functions-all-machine-learners-should-know-4fb140e9d4b0 https://www.youtube.com/watch?v=QBbC3Cjsnjg 🫱Core ML Concepts: https://www.educative.io/answers/definition-model-fitting https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/ https://ruveydakardelcetin.medium.com/the-importance-of-splitting-datasets-into-training-validation-and-test-sets-417caaeae91d https://www.simplilearn.com/tutorials/machine-learning-tutorial/cross-validation https://statology.org/leave-one-out-cross-validation/ https://medium.com/@mouadenna/time-series-splitting-techniques-ensuring-accurate-model-validation-5a3146db3088 https://youtu.be/EuBBz3bI-aA?si=aRSpNvSTcHTMYWGB https://ruveydakardelcetin.medium.com/the-importance-of-splitting-datasets-into-training-validation-and-test-sets-417caaeae91d https://www.simplilearn.com/tutorials/machine-learning-tutorial/regularization-in-machine-learning https://learn.epam.com/myLearning/eur01.safelinks.protection.outlook.com/GetUrlReputation https://www.geeksforgeeks.org/hyperparameter-tuning/ https://jakevdp.github.io/PythonDataScienceHandbook/05.03-hyperparameters-and-model-validation.html https://statisticsbyjim.com/regression/model-specification-variable-selection/ 🫱More Advanced GLMs https://christophm.github.io/interpretable-ml-book/extend-lm.html https://learn.epam.com/myLearning/eur01.safelinks.protection.outlook.com/GetUrlReputation https://online.stat.psu.edu/stat504/lesson/6/6.1 https://www.youtube.com/watch?v=6Nfv0Xr44y8 https://www.youtube.com/watch?v=i62gffPrZYA Robust regression https://machinelearningmastery.com/robust-regression-for-machine-learning-in-python/ https://online.stat.psu.edu/stat501/lesson/t/t.1/t.1.1-robust-regression-methods https://scikit-learn.org/stable/modules/linear_model.html#different-scenario-and-useful-concepts https://www.youtube.com/watch?v=ddHpy0ptUvI https://www.youtube.com/watch?v=Vf7oJ6z2LCc