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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.

This article displays the list of machine learning algorithms such as linear, logistic regression, kmeans, decision trees along with Python R code.

We are probably living in the most defining period of human history. The period when computing moved from large mainframes to PCs to cloud. But what makes it defining is not what has happened, but what is coming our way in years to come. What makes this period exciting for some one like me is the democratization of the tools and techniques, which followed the boost in computing.