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Index
Copyright 2020 Manning Publications
welcome
brief contents
preface
1 The Traveling Diabetes Clinic: A first take at the problem
1.1 The Traveling Diabetes Clinic Problem
1.1.1 Reading the data with pandas
1.2 A Simple ML Attempt with scikit-learn
1.2.1 Choosing a Model
1.2.2 Implementing the Model with scikit-learn
1.2.3 Establishing a Baseline
2 Grokking the Problem: What does the data look like?
2.1 Populations and Samples
2.2 Descriptive Statistics
2.2.1 Mean, Mode, and Median
2.2.2 Ranges, Sample Variance, and Sample Standard Deviation
2.2.3 Histogram Plots
3 Grokking Deeper: Where did the data come from?
3.1 Probability and Distributions
3.1.1 Random Variables, Distributions, and their Properties
3.1.2 How to read math?
3.1.3 Expectation, Variance, and Estimations
3.2 Conditional Probability
3.2.1 The Bayes Rule
3.2.2 Independent Random Variables
3.3 Applying the Naive Bayes Model with scikit-learn
4 Setting the Stage
4.1 Generative and Discriminative Models
4.1.1 Generative Models
4.1.2 Discriminative Models and the Target Function
4.1.3 Which Is Better?
4.2 Types of Machine Learning Problems
Prelude
5 K-Nearest Neighbors Method
5.1 A Basic K-NN Classifier
5.1.1 The “Can I eat that?” App
5.1.2 The Intuition Behind k-NN
5.1.3 How to Measure Similarity?
5.1.4 k-NN in Action
5.1.5 Boosting Performance with NumPy
5.2 A Better k-NN Classifier
5.2.1 Doing Faster Neighborhood Search Using K-d trees
5.2.2 Using k-d Trees with scikit-learn
5.2.3 Tuning the Value of k
5.2.4 Choosing the Metric
5.3 Is K-nn Reliable?
5.3.1 The Bayes Optimal Classifier
5.3.2 Reliability of 1-NN
6 K-Means Clustering
6.1 A New Marketing Plan for a Wholesale Distributor
6.1.1 The K-means Method
6.1.2 All Features Shall be Equal
6.1.3 Applying K-means with scikit-learn
6.2 Tuning the Value of k with Silhouette Score
6.2.1 Creating Marketing Plans Against the Detected Customers Segments
6.3 Limitations of K-means
6.3.1 The Math Beyond the Circular Tendency
7 Decision Trees
7.1 Predicting the Price of a Used Car
7.1.1 Modeling the Problem with Decision Tress
7.1.2 How to Build a Decision Tree
7.1.3 Coding a Primitive Decision Tree
7.2 Training a Decision Tree with scikit-learn
7.2.1 Preparing the Data
7.2.2 Training and Evaluating the Decision Tree
7.3 Trim the Tree or Grow Yourself Forest
7.3.1 Pruning the Tree
7.3.2 Random Forests
7.4 What Controls Generalization?
7.4.1 Why do Machines Learn from Data?
7.4.2 Generalization Bounds
7.4.3 The Bias-Variance Trade-off
7.4.4 Why do Random Forests Work so Well?
A Appendix
A.1 Installing the Anaconda Distribution
A.2 Working with Jupyter Notebooks
A.2.1 Exploring a Jupyter Notebook
A.2.2 Markdown Cells
A.2.3 Code Cells
A.2.4 How does all this work?
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