H ENRIK B RINK J OSEPH W. R ICHARDS M ARK F ETHEROLF - Real-World Machine Learning [2017, PDF, ENG]

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
4.4.2. Grid Search
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