In this 3-part series of articles, you will gain an intuitive understanding of some fundamental concepts in machine learning such as: Building blocks of curves Non-linear regression Curve fitting and overfitting Regularization to prevent overfitting Hyper-parameters in machine learning Cross-validation to fine-tune models You will also get hands-on practice to understand these concepts better. For this

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In the previous article, we started with the theme that overfitting is an inherent problem in machine learning associated with big data. Essentially, if you have many variables and their polynomial terms (X-variables) in a model you could fit any response data (y-variable) to perfection. This perfect fit for the observed data is overfitting since this model will

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In the last part of this series on fundamental machine learning, you learned about regularization and cross-validation. Here, you will gain a sound understanding of model hyper-parameter tuning to develop robust models. The machines do learn but they still need a good human tutor. In the last part, you were also introduced to my paternal grandmother to

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