Machine Learning with scikit-learn Quick Start Guide

Table of contents

1. Title Page
2. Copyright and Credits
   1. Machine Learning with scikit-learn Quick Start Guide
3. Dedication
4. About Packt
   1. Why subscribe?
   2. Packt.com
5. Contributors
   1. About the author
   2. About the reviewers
   3. Packt is searching for authors like you
6. Preface
   1. Who this book is for
   2. What this book covers
   3. To get the most out of this book
      1. Download the example code files
      2. Code in action
      3. Conventions used
   4. Get in touch
      1. Reviews
7. Introducing Machine Learning with scikit-learn
   1. A brief introduction to machine learning
      1. Supervised learning
      2. Unsupervised learning
   2. What is scikit-learn?
   3. Installing scikit-learn
      1. The pip method
      2. The Anaconda method
      3. Additional packages
         1. Pandas
         2. Matplotlib
         3. Tree
         4. Pydotplus
         5. Image
   4. Algorithms that you will learn to implement using scikit-learn
      1. Supervised learning algorithms
      2. Unsupervised learning algorithms
   5. Summary
8. Predicting Categories with K-Nearest Neighbors
   1. Technical requirements
   2. Preparing a dataset for machine learning with scikit-learn
      1. Dropping features that are redundant
      2. Reducing the size of the data
      3. Encoding the categorical variables
      4. Missing values
   3. The k-NN algorithm
   4. Implementing the k-NN algorithm using scikit-learn
      1. Splitting the data into training and test sets
      2. Implementation and evaluation of your model
   5. Fine-tuning the parameters of the k-NN algorithm
   6. Scaling for optimized performance
   7. Summary
9. Predicting Categories with Logistic Regression
   1. Technical requirements
   2. Understanding logistic regression mathematically
   3. Implementing logistic regression using scikit-learn
      1. Splitting the data into training and test sets
   4. Fine-tuning the hyperparameters
   5. Scaling the data
   6. Interpreting the logistic regression model
   7. Summary
10. Predicting Categories with Naive Bayes and SVMs
    1. Technical requirements
    2. The Naive Bayes algorithm
       1. Implementing the Naive Bayes algorithm in scikit-learn
    3. Support vector machines
       1. Implementing the linear support vector machine algorithm in scikit-learn
       2. Hyperparameter optimization for the linear SVMs
          1. Graphical hyperparameter optimization
          2. Hyperparameter optimization using GridSearchCV
       3. Scaling the data for performance improvement
    4. Summary
11. Predicting Numeric Outcomes with Linear Regression
    1. Technical requirements
    2. The inner mechanics of the linear regression algorithm
    3. Implementing linear regression in scikit-learn
       1. Linear regression in two dimensions
       2. Using linear regression to predict mobile transaction amount
       3. Scaling your data
    4. Model optimization
       1. Ridge regression
       2. Lasso regression
    5. Summary
12. Classification and Regression with Trees
    1. Technical requirements
    2. Classification trees
       1. The decision tree classifier
          1. Picking the best feature
          2. The Gini coefficient
          3. Implementing the decision tree classifier in scikit-learn
          4. Hyperparameter tuning for the decision tree
          5. Visualizing the decision tree
       2. The random forests classifier
          1. Implementing the random forest classifier in scikit-learn
          2. Hyperparameter tuning for random forest algorithms
       3. The AdaBoost classifier
          1. Implementing the AdaBoost classifier in scikit-learn
          2. Hyperparameter tuning for the AdaBoost classifier
    3. Regression trees
       1. The decision tree regressor
          1. Implementing the decision tree regressor in scikit-learn
          2. Visualizing the decision tree regressor
       2. The random forest regressor
          1. Implementing the random forest regressor in scikit-learn
       3. The gradient boosted tree
          1. Implementing the gradient boosted tree in scikit-learn
    4. Ensemble classifier
       1. Implementing the voting classifier in scikit-learn
    5. Summary
13. Clustering Data with Unsupervised Machine Learning
    1. Technical requirements
    2. The k-means algorithm
       1. Assignment of centroids
       2. When does the algorithm stop iterating?
    3. Implementing the k-means algorithm in scikit-learn
       1. Creating the base k-means model
       2. The optimal number of clusters
    4. Feature engineering for optimization
       1. Scaling
       2. Principal component analysis
    5. Cluster visualization
       1. t-SNE
       2. Hierarchical clustering
          1. Step 1 – Individual features as individual clusters
          2. Step 2 – The merge
          3. Step 3 – Iteration
          4. Implementing hierarchical clustering
    6. Going from unsupervised to supervised learning
       1. Creating a labeled dataset
       2. Building the decision tree
    7. Summary
14. Performance Evaluation Methods
    1. Technical requirements
    2. Why is performance evaluation critical?
    3. Performance evaluation for classification algorithms
       1. The confusion matrix
       2. The normalized confusion matrix
       3. Area under the curve
       4. Cumulative gains curve
       5. Lift curve
       6. K-S statistic plot
       7. Calibration plot
       8. Learning curve
       9. Cross-validated box plot
    4. Performance evaluation for regression algorithms
       1. Mean absolute error
       2. Mean squared error
       3. Root mean squared error
    5. Performance evaluation for unsupervised algorithms
       1. Elbow plot
    6. Summary
15. Other Books You May Enjoy
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