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Machine learning and statistical concepts can be join in 7 categories: Tree Based Methods, Linear (Regularized) Models, Hypothesis Testing & confidence, Resampling Methods, Clustering K-means, Feature Selection and Evaluation Metrics.

a. Decision Trees
Please check https://medium.com/data-science-group-iitr/decision-trees-decoded-c70b4f7ff542

b. Random Forest
Please check https://medium.com/@williamkoehrsen/random-forest-simple-explanation-377895a60d2d

c. Gradient Boosting Trees
Please check https://towardsdatascience.com/understanding-gradient-boosting-machines-9be756fe76ab

a. Lasso & Ridge
Please check https://www.analyticsvidhya.com/blog/2016/01/ridge-lasso-regression-python-complete-tutorial/

b. Linear Regression
Please check https://towardsdatascience.com/linear-regression-detailed-view-ea73175f6e86

c. Logistic Regression
Please check https://towardsdatascience.com/logistic-regression-detailed-overview-46c4da4303bc

a. A/B Testing
Please check https://hackernoon.com/a-b-testing-youre-doing-it-wrong-77d628ac9518

b. Chi Square Test
Please check https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/chi-square/

c. Statistical Tests
Please check https://towardsdatascience.com/statistical-tests-when-to-use-which-704557554740

a. Bootstrapping and Bagging
Please check https://www.quora.com/What-is-the-difference-between-bagging-and-bootstrapping-in-ML-statistics

b. Cross Validation
Please check https://machinelearningmastery.com/k-fold-cross-validation/

Please check https://towardsdatascience.com/clustering-using-k-means-algorithm-81da00f156f6

Please check https://towardsdatascience.com/why-how-and-when-to-apply-feature-selection-e9c69adfabf2

a. Classification Metrics
Please check https://towardsdatascience.com/the-5-classification-evaluation-metrics-you-must-know-aa97784ff226

b. Regression Metrics
Please check https://medium.com/usf-msds/choosing-the-right-metric-for-machine-learning-models-part-1-a99d7d7414e4

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