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This is the final and concluding part of my series on ‘Practical Machine Learning with R and Python’. Included are Machine Learning algorithms in R and Python. The algorithms implemented are:

1. Practical Machine Learning with R and Python – Part 1 The student will learn regression of a continuous target variable. Specifically Univariate, Multivariate, Polynomial regression and KNN regression in both R and Python.
2. Practical Machine Learning with R and Python – Part 2  The Focus is on Logistic Regression, KNN classification and Cross Validation error for both LOOCV and K-Fold in both R and Python.
3. Practical Machine Learning with R and Python – Part 3 This 3rd part includes feature selection in Machine Learning. Specifically, best fit, forward fit, backward fit, ridge(L2 regularization) & lasso (L1 regularization). It contains equivalent code in R and Python.
4. Practical Machine Learning with R and Python – Part 4 In this part, SVMs, Decision Trees, Validation, Precision-Recall, AUC and ROC curves are being discussed.
5. Practical Machine Learning with R and Python – Part 5  This part touches upon B-splines, natural splines, smoothing splines, Generalized Additive Models (GAMs), Decision Trees, Random Forests and Gradient Boosted Trees.
6. Practical Machine Learning with R and Python - Part6 This last part covers Unsupervised Machine Learning, specifically the implementations of Principal Component Analysis (PCA), K-Means and Heirarchical Clustering. The R Markdown file can be downloaded from Github.