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
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