Description
PEARSON INDIA Machine Learning In Production, 1/E by Andrew Kelleher
Machine Learning in Production is a crash course in data science and machine learning for learners who
need to solve real-world problems in production environments. Written for technically competent
"accidental data scientists" with more curiosity and ambition than formal training, this complete and
rigorous introduction stresses practice, not theory.
Building on agile principles, Andrew and Adam Kelleher show how to quickly deliver signi¬cant value in
production, resisting overhyped tools and unnecessary complexity. Drawing on their extensive
experience, they help you ask useful questions and then execute production projects from start to -nish.
The authors show just how much information you can glean with straightforward queries, aggregations,
and visualizations, and they teach indispensable error analysis methods to avoid costly mistakes. They
turn to workhorse machine learning techniques such as linear regression, classi¬cation, clustering, and
Bayesian inference, helping you choose the right algorithm for each production problem. Their
concluding section on hardware, infrastructure, and distributed systems o ers unique and invaluable
guidance on optimization in production environments.
They always focus on what matters in production: solving the problems that o er the highest return on
investment, using the simplest, lowest-risk approaches that work.
Post Review
Table of Content
Chapter 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 Nodes
Salient Features
1. ? Leverage agile principles to maximize development e_ciency in production projects
2. ? Learn from practical Python code examples and visualizations that bring essential algorithmic concepts to life
3. ? Start with simple heuristics and improve them as your data pipeline matures
4. ? Communicate your results with basic data visualization techniques
5. ? Master basic machine learning techniques, starting with linear regression and random forests
6. ? Perform classi_cation and clustering on both vector and graph data
7. ? Learn the basics of graphical models and Bayesian inference
8. ? Understand correlation and causation in machine learning models
9. ? Explore over_tting, model capacity, and other advanced machine learning techniques
10. ? Make informed architectural decisions about storage, data transfer, computation, and communicat