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Machine Learning Engineering by Andriy Burkov is a science book available to read on EtoBox.
What is Machine Learning Engineering about?
Foreword Preface Who This Book is For How to Use This Book Should You Buy This Book? Introduction Notation and Definitions Data Structures Capital Sigma Notation What is Machine Learning Supervised Learning Unsupervised Learning Semi-Supervised Learning Reinforcement Learning Data and Machine Learning Terminology Data Used Directly and Indirectly Raw and Tidy Data Training and Holdout Sets Baseline Machine Learning Pipeline Parameters vs. Hyperparameters Classification vs. Regression Model-Based vs. Instance-Based Learning Shallow vs. Deep Learning Training vs. Scoring When to Use Machine Learning When the Problem Is Too Complex for Coding When the Problem Is Constantly Changing When It Is a Perceptive Problem When It Is an Unstudied Phenomenon When the Problem Has a Simple Objective When It Is Cost-Effective When Not to Use Machine Learning What is Machine Learning Engineering Machine Learning Project Life Cycle Summary Before the Project Starts Prioritization of Machine Learning Projects Impact of Machine Learning Cost of Machine Learning Estimating Complexity of a Machine Learning Project The Unknowns Simplifying the Problem Nonlinear Progress Defining the Goal of a Machine Lear
Who reads Machine Learning Engineering?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- Andriy Burkov
- Publisher
- True Positive Inc.
- Published
- 2020
- Language
- EN
- ISBN
- 9781777005450
- Category
- science
- Subjects
- Computer Science, Stem
- Rating
- 4.7 / 5 (243 ratings)
- Updated
- 2026-03-14
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