Aug 27, 2024 · Machine Learning Algorithms in Depth dissects and explains dozens of algorithms across a variety of applications, including finance, computer vision, and NLP. Each algorithm is mathematically derived, followed by its hands-on Python implementation along with insightful code annotations and informative graphics.

Machine Learning Algorithms in Depth dissects and explains dozens of algorithms across a variety of applications, including finance, computer vision, and NLP. Each algorithm is mathematically derived, followed by its hands-on Python implementation along with insightful code annotations and informative graphics.

The figure on the cover of Machine Learning Algorithms in Depth, titled “Maraichere,” or “Vegetable Grower,” is taken from a book by Louis Curmer published in 1841. Each illustration is finely drawn and colored by hand.

Understanding ML algorithms from scratch will help you choose the right algorithm for the task, explain the results, troubleshoot advanced problems, extend algorithms to new applications, and improve the performance of existing algorithms.

Jan 16, 2024 · Generally speaking, Machine Learning involves studying computer algorithms and statistical models for a specific task using patterns and inference instead of explicit instructions. And there is no doubt that Machine Learning is an insanely popular career choice today.

In this article, we share the 13 best machine learning books in 2024. Whether you’d like to land a job as a machine learning engineer or want to further your data science career by learning new skills, we’ve included up-to-date machine learning books for …

Aug 24, 2021 · For the veteran and new machine learning specialists who are looking to implement solutions to real-world machine learning problems, this book thoroughly discusses the various applications of machine and deep learning techniques.

Jun 13, 2022 · It is possible to design and deploy advanced machine learning algorithms that are essentially math-free and stats-free. People working on that are typically professional mathematicians. These algorithms are not necessarily simpler. See for instance a math-free regression technique with prediction intervals, here.