Coding Machine Learning. Hello Folks, here I present the first coding demonstration lecture, based on my 1st lecture on Probabilistic Machine Learning. Here I write the code from scratch, discuss and analyze the results, which were covered in details in the whiteboard classes. What we cover? -Random Variables, and validating law of large numbers. -Visualizing a dataset -Doing an EDA on Iris dataset and understanding the correlation among features. -Classifier basics -Empirical Risk Minimization and Generalization. -Epistemic and Aleatoric Uncertainties. -Softmax Function and LogSumExp Trick to avoid overflow issues -Linear Models -Maximum Likelihood Estimation. -Simple end to end ML pipeline Function. While writing the code, my intent is to ensure that concepts are understood with crystal clarity. These code demonstrations are specific to my theory ML lectures, and link is attached. Theory-Intuition-Code Implementation Link : https://youtu.be/X_yOlx8Zp4g Theory Lecture Link : https://youtu.be/kMkCOrp8te8 submitted by /u/Negative_War_65
Originally posted by u/Negative_War_65 on r/ArtificialInteligence