Requirements: .ePUB reader, 20.8 MB
Overview: Many believe that a Machine Learning model, once trained, can act autonomously. This misconception has hindered innovation in ML/AI for far too long. In reality, ML models require integration within a comprehensive system encompassing inputs, processing, and outputs. My new book, “Applied Machine Learning: A Practical Guide from Novice to Pro,” is designed to reshape your understanding of Machine Learning and its practical applications. This hands-on guide will help you comprehend what Machine Learning truly is and how it can transform your business operations. Machine Learning is an immensely intriguing field that involves harnessing computational algorithms to glean insights and make predictions from data without explicit programming. It’s a subset of artificial intelligence focused on creating systems capable of learning and improving autonomously from experience. To wield machine learning effectively, data scientists, or aspiring ones, must grasp its principles and inner workings thoroughly. This book serves as a comprehensive guide to various Machine Learning techniques widely employed in Data Science and analytics. It traverses the entire machine learning pipeline, commencing from data preprocessing and c ulminating in model selection. The text delves into diverse Regression models, including Simple, Multiple, Polynomial, Support vector, Decision tree, and Random forest regression models, alongside evaluating these models and selecting the optimal regression model for a given task.
Genre: Non-Fiction > Tech & Devices
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