FYI, pretty simple stuff. — One bit of advice: it is important to view knowledge as sort of a semantic tree — make sure you understand the fundamental principles, i.e. the trunk and big branches, before you get into the leaves/details or there is nothing for them to hang on to. — Elon Musk PSA…

“From the beginning to the end, losers lose, winners win” — 50 Cent — I was reading Buddhism Plain and Simple by Steve Hagen. First off, this book offers a tremendous harsh reality — you will die. We successfully occupy our minds with non-priority tasks. How many times have you commuted to work without realizing you were on auto-pilot? You probably don’t even remember…

I promise it’s not just another “ML Article.” — Terminology: Gini Impurity: Measurement of the likelihood of incorrect classification of a new instance of a random variable, if that new instance were randomly classified according to the distribution of class labels from the data set. Information Gain: It calculates how important each independent variable is (features). Entropy: A measurement of…

I promise it’s not just another “ML Article.” — Terminology: Variance: It measures how far a data set is spread out. Mean: A calculated “central” value of a set of numbers. Standard Deviation: Similar to variance, but it is the root value of the variation. The coefficient of Variation (CV): A measure of relative variability. The ratio of the standard…

I promise it’s not just another “ML Article.” — Terminology: Vectors: A vector is composed of a magnitude and direction. Geometrically, a vector in a 2-Dimensional plane (x and y graph) is a line from the origin to its coordinates. …

I promise it’s not just another “ML Article.” — Terminology: Euclidean Distance: The distance between data “points” (p1, p2, …, pn). It computes the square root of the sum of the squares of the differences between the data “points.” Manhattan Distance: The distance between data “points” (p1, p2, …, pn). …

I promise it’s not just another “ML Article.” — Terminology: Naive Bayes: The Naive Bayes Classifier technique derives from on the Bayesian theorem. Bayes Theorem: Bayes’ theorem is a mathematical equation used in probability and statistics to calculate the conditional probability. In other words, it is used to calculate the probability of an event based on its association with another…

I promise it’s not just another “ML Article.” — Terminology: Supervised Learning: Analyzed the dataset to produce a predicted function which will be used for forecasting new examples. Ground Truth: The actual result. Cross-Entropy: “Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. …

I promise it’s not just another “ML Article.” — Terminology: Supervised Learning: Analyzed the dataset to produce a predicted function to forecast new examples. Overfitting: A model that has learned ‘too much’ of the dataset. Hence, the model will not be as useful on new examples. Ground Truth: The actual result. MSE: Mean Squared Error. It’s a formula that measures…