We believe that you can learn machine learning fundamentals in 2 weeks. We have both created and curated content for users of all backgrounds. To service this need, we have designed a book for anyone interested in machine learning, regardless of background. If you want to learn more about our curriculum as a whole, check out our curriculum page.
While most machine learning textbooks and guides are usually upwards of 30 USD, we believe in accessibility to knowledge. To reinforce this message, we have placed our book at a friendly price point. You can buy a paperback copy of our book for only 14.99 USD
A Guide to Machine Learning, Deep Learning, and their Applications acts as a comprehensive guide to the innovative fields of machine learning and deep learning. The tutorial-style prose allows the reader to explore the topics from scratch and to broaden their knowledge of the theory and for practical understanding. By the time you are finished with this book, you will be confident with your knowledge of what machine learning is and how state-of-the-art algorithms can be utilized to solve real-world problems. Whether you are a student (high school, undergraduate, or graduate) or a data scientist, this book will supplement your current knowledge with rigorous yet practical tools to break down machine learning and familiarize yourself with best practices used in the industry.
In today’s world, machine learning is becoming increasingly ubiquitous as the forefront of methods for pattern recognition. These techniques require input data and allow the computer to learn from experience. Through the recent advances in data science software and the availability of libraries, it is now possible for programmers and basic users to get familiar with creating their own programs that are capable of learning from the data. Deep learning is the fastest-growing subset of machine learning and is loosely inspired by biological networks.
This book serves as a guide for both beginners and users of machine learning to become more familiar with the fields on both a theoretical basis and through a hands-on, practical example-based approach. Unlike other books on the subject, we use efficient, grounded tools to demonstrate the power of machine learning while guiding the reader from the fundamentals of data science and mathematics towards state-of-the-art algorithms and modern developments in the field. What’s special about this book is that the applications are at the center of the content and detailed commentary is present in every chapter. We also prioritize high-level, concrete explanations over less intuitive pathways to understanding. Moreover, this book was written by students and is for students.
With just knowledge of high school mathematics, readers can follow along. No knowledge of calculus, linear algebra, or programming is required, just a willingness to learn. This book builds understanding through intuitive explanations and direct applications. Through this content, you will learn both challenging yet elegant concepts to practice the application of machine learning to computer vision, natural language processing, and bioinformatics.
We held these titles in July 2020 and continue to hold spots on both the best sellers and new releases lists on Amazon.
#1 best new release in neural networks #1 best new release in AI and semantics #24 in AI and Machine Learning BEST SELLERS #3 in New Releases for computer science #1 in AI and Machine Learning new releases #6 in Neural Networks BEST SELLERS #21 in AI and Semantics BEST SELLERS #58 in top computer science best sellers in the world
“We live in a golden age of machine learning with so many inventions happening and so much more left to do. At this juncture, it is of critical importance that machine learning is understood and accessible to students from all backgrounds and perspectives. It is exciting to see a high school student such as Siddharth putting the effort to help bridge the gap in demystifying machine learning for high school students.”
“This book is a tour de force through the scientific aspects of AI by a group of impressive young authors. I strongly recommend it.”