Video description
In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.
"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
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
-
PART 1. THE MACHINE-LEARNING WORKFLOW
- Chapter 1. What is machine learning?
- Chapter 1. Using data to make decisions
- Chapter 1. The machine-learning approach
- Chapter 1. Five advantages to machine learning
- Chapter 1. Following the ML workflow: from data to deployment
- Chapter 1. Boosting model performance with advanced techniques
- Chapter 2. Real-world data
- Chapter 2. Which features should be included?
- Chapter 2. How much training data is required?
- Chapter 2. Preprocessing the data for modeling
- Chapter 2. Simple feature engineering
- Chapter 2. Using data visualization
- Chapter 2. Density plots
- Chapter 3. Modeling and prediction
- Chapter 3. Finding the relationship between input and target
- Chapter 3. Classification: predicting into buckets
- Chapter 3. Classifying complex, nonlinear data
- Chapter 3. Regression: predicting numerical values
- Chapter 3. Summary
- Chapter 4. Model evaluation and optimization
- Chapter 4. The solution: cross-validation
- Chapter 4. Evaluation of classification models
- Chapter 4. Accuracy trade-offs and ROC curves
- Chapter 4. Evaluation of regression models
- Chapter 4. Model optimization through parameter tuning
- Chapter 4. Summary
- Chapter 5. Basic feature engineering
- Chapter 5. Basic feature-engineering processes
- Chapter 5. Feature selection
- Chapter 5. Forward selection and backward elimination
- Chapter 5. Summary
-
PART 2. PRACTICAL APPLICATION
- Chapter 6. Example: NYC taxi data
- Chapter 6. Defining the problem and preparing the data
- Chapter 6. Modeling
- Chapter 6. Summary
- Chapter 7. Advanced feature engineering
- Chapter 7. Topic modeling
- Chapter 7. Content expansion
- Chapter 7. Image features
- Chapter 7. Extracting objects and shapes
- Chapter 7. Time-series features
- Chapter 7. Classical time-series features
- Chapter 7. Summary
- Chapter 8. Advanced NLP example: movie review sentiment
- Chapter 8. So what’s the use case?
- Chapter 8. Extracting basic NLP features and building the initial model
- Chapter 8. Normalizing bag-of-words features with the tf-idf algorithm
- Chapter 8. Advanced algorithms and model deployment considerations
- Chapter 9. Scaling machine-learning workflows
- Chapter 9. Subsampling training data in lieu of scaling?
- Chapter 9. Scaling ML modeling pipelines
- Chapter 9. Scaling predictions
- Chapter 9. Summary
- Chapter 10. Example: digital display advertising
- Chapter 10. Feature engineering and modeling strategy
- Chapter 10. Singular value decomposition
- Chapter 10. Modeling
- Chapter 10. Summary
Product information
- Title: Real-World Machine Learning video edition
- Author(s):
- Release date: September 2016
- Publisher(s): Manning Publications
- ISBN: None
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