Overview

The AI age is now fully in swing with the largest companies in the world all building AI hardware, developing AI software, or shipping AI-powered products. Of the current top ten companies by market capitalization, almost all are core components of the AI ecosystem. While large language models like ChatGPT and Claude are the current stars of the AI world, these products are all built atop a solid foundation of machine learning. Basic machine learning skills are thus crucial in the current technology workplace and will only become more essential in the future. Who is this book for? This book is written for engineers, machine learning scientists, data scientists, and technologists working with machine learning or trying to break into the field. It is focused on the foundational ML concepts that one must be familiar with in order to demonstrate technical proficiency and to be able to converse effectively with an interviewer. Table of Contents (in alphabetical order) A ⇒ Adam B ⇒ Backpropagation C ⇒ ConvNets D ⇒ Decision Trees E ⇒ Ensembling F ⇒ Feature Scaling G ⇒ Gradient Descent H ⇒ Huber Loss I ⇒ Information Retrieval J ⇒ JAX K ⇒ k-Means L ⇒ Logistic Regression M ⇒ Multinomial LR N ⇒ Naive Bayes O ⇒ Online Learning P ⇒ Perceptron Q ⇒ Q-Learning R ⇒ Random Forests S ⇒ SVM T ⇒ t-SNE U ⇒ Underfitting V ⇒ Validation Set W ⇒ Weight Initialization X ⇒ XGBoost Y ⇒ YOLO Z ⇒ Zero-Shot Learning

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