Executive Summary Week 1: Introduction Let’s say you have a data set with six houses, so you know the size of the houses in square feet or square meters and you know the price of the house and you want to fit a function to predict the price of a house as Read more…
Amid the AI revolution, diverse AI models like large language and generative AI models have come into the limelight. These novel AI models require efficient data processing, achievable using vector embeddings. By providing semantic information to the AI models, they gain a better understanding and can perform complex tasks efficiently. Read more…
Introduction to Building Machine Learning Applications Building machine learning applications requires a thorough understanding of the fundamentals of machine learning and software development. This section will provide an overview of the key considerations and best practices for building machine learning applications. Best Practices for Data Preprocessing Data preprocessing is a Read more…
Median Absolute deviation is one of the other techniques specifically used for analyzing the performance of regression models. Computing MAD of errors For a univariate data set X1, X2, …, Xn, the MAD is defined as the median of the absolute deviations from the data’s median, X_median = median(X) Median Read more…
R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple regression. This metric is specifically designed for regression-based algorithms where the output is a real value. Computing Read more…
We can plot error distributions like probability density functionand cumulative density function and make important deductionsbased on it. We can use plot Probability Density functions(PDF) and Cumulative density function (CDF) by using the error function as a random variable Using PDF of error distribution An ideal pdf for error distributions Read more…
Logarithmic loss (or cross-entropy) measures the performance of a classification model where the prediction input is a probability value between 0 and 1. The goal of our machine learning models is to minimize this value. It is also heavily used in Kaggle competitions to estimate the score of submissions. A Read more…
For binary classification problems, a good way to measure the performance of a model is by finding out AUC (Area Under The Curve) of ROC (Receiver Operating Characteristics). What is a ROC curve? It is a plot of True Positive Rate(TPR) vs FPR at various thresholding levels. Let’s understand this Read more…
Confusion Matrix Confusion Matrix is a group or matrix of metrics in supervised learning scenarios which determine how good a model is in predictions. Lets consider a binary classifier with results 1 or 0. A confusion matrix here will be a 2×2 matrix where along one axis you have actual Read more…
Hypothesis Testing Hypothesis tests, also called significance tests, are ubiquitous in the traditional statistical analysis of published research. Their purpose is to help you learn whether random chance might be responsible for an observed effect. KEY TERMS Null hypothesis The hypothesis that chance is to blame. Alternative hypothesis The counterpoint Read more…