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
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
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
If you want to gain a sound understanding of machine learning then you must know gradient descent optimization. In this article, you will get a detailed and intuitive understanding of gradient descent to solve machine learning algorithms. The entire tutorial uses images and visuals to make things easy to grasp. Here, we will use an example
How do machines learn? They learn the same way as humans. Humans learn from experience and so do machines. For machines, the experience is in the form of data. Machines use powerful algorithms to make sense of the data. They identify underlining patterns within the data to learn things about the world. Like humans, machines