AI From Scratch (Paperback)
Miras Zharas
Sold by Grand Eagle Retail, Bensenville, IL, U.S.A.
AbeBooks Seller since October 12, 2005
New - Soft cover
Condition: New
Ships within U.S.A.
Quantity: 1 available
Add to basketSold by Grand Eagle Retail, Bensenville, IL, U.S.A.
AbeBooks Seller since October 12, 2005
Condition: New
Quantity: 1 available
Add to basketPaperback. New Horizons: Introduction to Machine Learning is a comprehensive and accessible book for beginners in Artificial Intelligence. It provides essential skills for an aspiring Machine Learning engineer, and serves as an effective tool to break into the field of AI. Whether you are a student, a professional looking for a career switch, this book offers a step-by-step journey through the foundation of Artificial Intelligence.In this book, you will explore the evolution of AI, starting from its pioneers like Alan Turing to the latest breakthroughs in the field. With clear explanations and comments, you will learn fundamental algorithms that form a foundation for modern models, such as Linear Regression, Binary Classification/Logistic Regression and ways to deal with challenges that a Machine Learning engineer faces everyday. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Seller Inventory # 9798261755708
New Horizons: Introduction to Machine Learning is a comprehensive and accessible book for beginners in Artificial Intelligence. It provides essential skills for an aspiring Machine Learning engineer, and serves as an effective tool to break into the field of AI. Whether you are a student, a professional looking for a career switch, this book offers a step-by-step journey through the foundation of Artificial Intelligence.
In this book, you will explore the evolution of AI, starting from its pioneers like Alan Turing to the latest breakthroughs in the field. With clear explanations and comments, you will learn fundamental algorithms that form a foundation for modern models, such as Linear Regression, Binary Classification/Logistic Regression and ways to deal with challenges that a Machine Learning engineer faces everyday.
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