Real-World Machine Learning video edition

Video description

"This is that crucial other book that many old hands wish they had back in the day."
Beau Cronin, 21 Inc.

Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. It will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you'll build skills in data acquisition and modeling, classification, and regression. You'll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you're done, you'll be ready to successfully build, deploy, and maintain your own powerful ML systems.

Machine learning systems help you find valuable insights and patterns in data, which you'd never recognize with traditional methods. In the real world, ML techniques give you a way to identify trends, forecast behavior, and make fact-based recommendations. It's a hot and growing field, and up-to-speed ML developers are in demand.
Inside:

  • Predicting future behavior
  • Performance evaluation and optimization
  • Analyzing sentiment and making recommendations
No prior machine learning experience assumed. Learners should know Python.

Henrik Brink, Joseph Richards, and Mark Fetherolf are experienced data scientists engaged in the daily practice of machine learning.

A comprehensive guide on how to prepare data for ML and how to choose the appropriate algorithms.
Michael Lund, iCodeIT

Very approachable. Great information on data preparation and feature engineering, which are typically ignored.
Robert Diana, RSI Content Solutions

NARRATED BY LISA FARINA

Table of contents

  1. PART 1. THE MACHINE-LEARNING WORKFLOW
    1. Chapter 1. What is machine learning?
    2. Chapter 1. Using data to make decisions
    3. Chapter 1. The machine-learning approach
    4. Chapter 1. Five advantages to machine learning
    5. Chapter 1. Following the ML workflow: from data to deployment
    6. Chapter 1. Boosting model performance with advanced techniques
    7. Chapter 2. Real-world data
    8. Chapter 2. Which features should be included?
    9. Chapter 2. How much training data is required?
    10. Chapter 2. Preprocessing the data for modeling
    11. Chapter 2. Simple feature engineering
    12. Chapter 2. Using data visualization
    13. Chapter 2. Density plots
    14. Chapter 3. Modeling and prediction
    15. Chapter 3. Finding the relationship between input and target
    16. Chapter 3. Classification: predicting into buckets
    17. Chapter 3. Classifying complex, nonlinear data
    18. Chapter 3. Regression: predicting numerical values
    19. Chapter 3. Summary
    20. Chapter 4. Model evaluation and optimization
    21. Chapter 4. The solution: cross-validation
    22. Chapter 4. Evaluation of classification models
    23. Chapter 4. Accuracy trade-offs and ROC curves
    24. Chapter 4. Evaluation of regression models
    25. Chapter 4. Model optimization through parameter tuning
    26. Chapter 4. Summary
    27. Chapter 5. Basic feature engineering
    28. Chapter 5. Basic feature-engineering processes
    29. Chapter 5. Feature selection
    30. Chapter 5. Forward selection and backward elimination
    31. Chapter 5. Summary
  2. PART 2. PRACTICAL APPLICATION
    1. Chapter 6. Example: NYC taxi data
    2. Chapter 6. Defining the problem and preparing the data
    3. Chapter 6. Modeling
    4. Chapter 6. Summary
    5. Chapter 7. Advanced feature engineering
    6. Chapter 7. Topic modeling
    7. Chapter 7. Content expansion
    8. Chapter 7. Image features
    9. Chapter 7. Extracting objects and shapes
    10. Chapter 7. Time-series features
    11. Chapter 7. Classical time-series features
    12. Chapter 7. Summary
    13. Chapter 8. Advanced NLP example: movie review sentiment
    14. Chapter 8. So what’s the use case?
    15. Chapter 8. Extracting basic NLP features and building the initial model
    16. Chapter 8. Normalizing bag-of-words features with the tf-idf algorithm
    17. Chapter 8. Advanced algorithms and model deployment considerations
    18. Chapter 9. Scaling machine-learning workflows
    19. Chapter 9. Subsampling training data in lieu of scaling?
    20. Chapter 9. Scaling ML modeling pipelines
    21. Chapter 9. Scaling predictions
    22. Chapter 9. Summary
    23. Chapter 10. Example: digital display advertising
    24. Chapter 10. Feature engineering and modeling strategy
    25. Chapter 10. Singular value decomposition
    26. Chapter 10. Modeling
    27. Chapter 10. Summary

Product information

  • Title: Real-World Machine Learning video edition
  • Author(s): Henrik Brink, Joseph W. Richards, Mark Fetherolf
  • Release date: September 2016
  • Publisher(s): Manning Publications
  • ISBN: None