Machine Learning in Production
Kelleher, Andrew
Machine Learning in Production - Noida Pearson 2025 - xxiv, 229p. 24 x 18 cm
hapter 1: The Role of the Data Scientist
Chapter 2: Project Workflow
Chapter 3: Quantifying Error
Chapter 4: Data Encoding and Preprocessing
Chapter 5: Hypothesis Testing
Chapter 6: Data Visualization
Part II: Algorithms and Architectures
Chapter 7: Introduction to Algorithms and Architectures
Chapter 8: Comparison
Chapter 9: Regression
Chapter 10: Classification and Clustering
Chapter 11: Bayesian Networks
Chapter 12: Dimensional Reduction and Latent Variable Models
Chapter 13: Causal Inference
Chapter 14: Advanced Machine Learning
Part III: Bottlenecks and Optimizations
Chapter 15: Hardware Fundamentals
Chapter 16: Software Fundamentals
Chapter 17: Software Architecture
Chapter 18: The CAP Theorem
Chapter 19: Logical Network Topological Node
9789389588507
006.31 / KEL
Machine Learning in Production - Noida Pearson 2025 - xxiv, 229p. 24 x 18 cm
hapter 1: The Role of the Data Scientist
Chapter 2: Project Workflow
Chapter 3: Quantifying Error
Chapter 4: Data Encoding and Preprocessing
Chapter 5: Hypothesis Testing
Chapter 6: Data Visualization
Part II: Algorithms and Architectures
Chapter 7: Introduction to Algorithms and Architectures
Chapter 8: Comparison
Chapter 9: Regression
Chapter 10: Classification and Clustering
Chapter 11: Bayesian Networks
Chapter 12: Dimensional Reduction and Latent Variable Models
Chapter 13: Causal Inference
Chapter 14: Advanced Machine Learning
Part III: Bottlenecks and Optimizations
Chapter 15: Hardware Fundamentals
Chapter 16: Software Fundamentals
Chapter 17: Software Architecture
Chapter 18: The CAP Theorem
Chapter 19: Logical Network Topological Node
9789389588507
006.31 / KEL