Zero to Hundred ML: Climb the Machine Learning Ladder from Beginner to Boss in 100 Steps

Needhi, Jeyadev (Author)/ Muthu, Mohanraj (Author)/ Selvaraj, Manokar (Author)

ISBN 10: 9999329063 / ISBN 13: 9789999329064

Published by Eliva Press, 2025

About this Item

199 pages. 6.00x0.45x9.00 inches. In Stock.

Seller Inventory # x-9999329063

Bibliographic Details

• Title: Zero to Hundred ML: Climb the Machine Learning Ladder from Beginner to Boss in 100 Steps
• Publisher: Eliva Press
• Publication Date: 2025
• Language: English
• ISBN 10: 9999329063
• ISBN 13: 9789999329064
• Binding: Paperback
• Condition: Brand New
• Weight: 0.27 kilograms

About this title

Synopsis

Zero to Hundred ML: Climb the Machine Learning Ladder from Beginner to Boss in 100 Steps is your all-in-one playbook for mastering machine learning—crafted by real-world engineers and researchers from Appian, Amazon, Apple, and FedEx (A³F). Whether you're a total beginner or an experienced developer exploring ML, this book takes you on a self-paced, structured journey through 100 practical, project-based steps designed to build your expertise one layer at a time. The book starts with the very basics—Python programming, linear algebra, probability, and statistics—before moving into core machine learning concepts such as regression, classification, clustering, and evaluation metrics. It then dives deep into modern AI techniques including neural networks, CNNs, RNNs, transformers, and generative models like GANs and autoencoders. But this isn’t just another theory-heavy guide. Each chapter is designed to be actionable—with hands-on coding exercises, real datasets, and mini-projects that you can build, tweak, and deploy. You’ll create models for tasks like emotion detection, fraud detection, recommendation systems, forecasting, sentiment analysis, and even multimodal AI applications combining text and images. In the production phase, you'll learn how to build APIs, set up CI/CD pipelines, deploy models using tools like Docker and MLflow, and monitor for drift and performance in real-world environments. The final section focuses on careers: ML interview prep, resume building, portfolio creation, open-source contribution, and specialization tracks for MSCS or industry roles. Whether you're preparing for grad school, switching to a data role, or launching your own startup, this book equips you with the mindset, skills, and toolkit to thrive in the evolving world of ML.