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Machine Learning in Production

Machine Learning in Production

  • Andrew Kelleher
  • Adam Kelleher
  • Author: Andrew Kelleher
    • ISBN:9789389588507
    • 10 Digit ISBN:9389588502
    • Price:Rs. 479.00
    • Pages:256
    • Imprint:Pearson Education
    • Binding:Paperback
    • Status:Available

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    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.


    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 communication