Real-World Machine Learning
Henrik Brink, Joseph W. Richards, and Mark Fetherolf
Foreword by Beau Cronin
  • September 2016
  • ISBN 9781617291920
  • 264 pages
  • printed in black & white

This is that crucial other book that many old hands wish they had back in the day.

From the Foreword by Beau Cronin, 21 Inc.

Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems.

Listen to this book in liveAudio! liveAudio integrates a professional voice recording with the book’s text, graphics, code, and exercises in Manning’s exclusive liveBook online reader. Use the text to search and navigate the audio, or download the audio-only recording for portable offline listening. You can purchase or upgrade to liveAudio here or in liveBook.

About the Technology

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.

About the book

Real-World Machine Learning 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.

Table of Contents detailed table of contents

Part 1: The machine-learning workflow

1. What is machine learning?

1.1. How Machines Learn

1.2. Using Data to Make Decisions

1.2.1. Traditional Approaches

1.2.2. The Machine Learning Approach

1.2.3. Five Advantages to Machine Learning

1.2.4. Challenges

1.3. The ML workflow: from Data to Deployment

1.3.1. Learning a Model from Data

1.3.2. Evaluating Model Performance

1.3.3. Optimizing Model Performance

1.4. Boosting Model Performance with Advanced Techniques

1.4.1. Data Pre-processing and Feature Engineering

1.4.2. Improving models continuously with online methods

1.4.3. Scaling Models with Data Volume and Velocity

1.5. Summary

1.6. Terms from this chapter

2. Real-world data

2.1. Getting Started: Data Collection

2.1.1. Which features should be included?

2.1.2. Obtaining ground-truth for the target variable

2.1.3. How much training data is required?

2.1.4. Is the training set representative enough?

2.2. Pre-processing the data for modeling

2.2.1. Categorical features

2.2.2. Dealing with missing data

2.2.3. Simple feature engineering

2.2.4. Data normalization

2.3. Using Data visualization

2.3.1. Mosaic Plots

2.3.2. Boxplots

2.3.3. Density Plots

2.3.4. Scatterplots

2.4. Summary

2.5. Terms from this chapter

3. Modeling and prediction

3.1. Basic machine learning modeling

3.1.1. Finding the relationship between input and target

3.1.2. The purpose of finding a good model

3.1.3. Types of modeling methods

3.1.4. Supervised versus unsupervised learning

3.2. Classification: predicting into buckets

3.2.1. Building a classifier and making predictions

3.2.2. Classification on complex, nonlinear data

3.2.3. Classification with multiple classes

3.3. Regression: prediction of numerical values

3.3.1. Building a regressor and making predictions

3.3.2. Regression on complex, nonlinear data

3.4. Summary

3.5. Terms from this chapter

4. Model evaluation and optimization

4.1. Model generalization: assessing predictive accuracy for new data

4.1.1. The Problem: Over-fitting and Model Optimism

4.1.2. The Solution: Cross-validation

4.1.3. Some things to look out for when using cross-validation

4.2. Evaluation of classification models

4.2.1. Class-wise accuracy and the confusion matrix

4.2.2. Accuracy trade-offs and ROC curves

4.3. Evaluation of regression models

4.3.1. Simple regression performance metrics

4.3.2. Examining residuals

4.4. Model Optimization through Parameter Tuning

4.4.1. ML Algorithms and their Tuning Parameters

4.5. Summary

4.6. Terms from this chapter

5. Basic feature engineering

5.1. Motivation: Why is Feature Engineering Useful?

5.2. Basic feature engineering processes

5.2.1. Example: Event recommendation

5.2.2. Handling date and time features

5.2.3. Simple text features

5.3. Feature selection

5.3.1. Forwards selection and backwards elimination

5.3.2. Feature selection for data exploration

5.3.3. Real-world feature selection example

5.4. Summary

5.5. Terms from this chapter

Part 2: Practical application

6. Example: NYC taxi data

6.1. Data: NYC taxi trip and fare information

6.1.1. Visualizing the data

6.1.2. Defining the problem and preparing the data

6.2. Modeling

6.2.1. Basic linear model

6.2.2. Nonlinear classifier

6.2.3. Including categorical features

6.2.4. Including date-time features

6.2.5. Model insights

6.3. Summary

6.4. Terms from this chapter

7. Advanced feature engineering

7.1. Advanced text features

7.1.1. Bag of words

7.1.2. Topic modeling

7.1.3. Content expansion

7.2. Image features

7.2.1. Simple image features

7.2.2. Extracting objects and shapes

7.3. Time-series features

7.3.1. Classical Time-Series Features

7.3.2. Feature Engineering for Event Streams

7.4. Summary

7.5. Terms from this chapter

8. Advanced NLP example: movie review sentiment

8.1. Exploring the data and use case

8.1.1. A first glance at the dataset

8.1.2. Inspecting the dataset

8.1.3. So, what’s the use case?

8.2. Extracting basic NLP features and building the initial model

8.2.1. Bag-of-words features

8.2.2. Building the model with the Naive Bayes algorithm

8.2.3. Normalizing bag-of-words features with the tf-idf algorithm

8.2.4. Optimizing model parameters

8.3. Advanced algorithms and model deployment considerations

8.3.1. word2vec features

8.3.2. Random forest model

8.4. Summary

9. Scaling machine learning workflows

9.1. Before scaling up

9.1.1. Identifying important dimensions

9.1.2. Sub-sampling training data in lieu of scaling?

9.1.3. Scalable data management systems

9.2. Scaling ML modeling pipelines

9.2.1. Scaling learning algorithms

9.3. Scaling predictions

9.3.1. Scaling prediction volume

9.3.2. Scaling prediction velocity

9.4. Summary

10. Example: digital display advertising

10.1. Display Advertising

10.2. Digital Advertising Data

10.3. Feature Engineering and Modeling Strategy

10.4. Size and Shape of the Data

10.5. Singular Value Decomposition

10.6. Resource Estimation and Optimization

10.7. Modeling

10.8. K-nearest neighbors

10.9. Random forests

10.10. Other Real World Considerations

10.11. Summary

10.12. Terms from this chapter

10.13. Recap and Conclusion

Appendix A: Popular machine-learning algorithms

What's inside

  • Predicting future behavior
  • Performance evaluation and optimization
  • Analyzing sentiment and making recommendations

About the reader

No prior machine learning experience assumed. Readers should know Python.

About the authors

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

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