Data Science Programming All-in-One For Dummies

Book 3

Manipulating Data Using Basic Algorithms

Contents at a Glance

1. Chapter 1: Working with Linear Regression
   1. Considering the History of Linear Regression
   2. Combining Variables
   3. Manipulating Categorical Variables
   4. Using Linear Regression to Guess Numbers
   5. Learning One Example at a Time
2. Chapter 2: Moving Forward with Logistic Regression
   1. Considering the History of Logistic Regression
   2. Differentiating between Linear and Logistic Regression
   3. Using Logistic Regression to Guess Classes
   4. Switching to Probabilities
   5. Working through Multiclass Regression
3. Chapter 3: Predicting Outcomes Using Bayes
   1. Understanding Bayes' Theorem
   2. Using Naïve Bayes for Predictions
   3. Working with Networked Bayes
   4. Considering the Use of Bayesian Linear Regression
   5. Considering the Use of Bayesian Logistic Regression
4. Chapter 4: Learning with K-Nearest Neighbors
   1. Considering the History of K-Nearest Neighbors
   2. Learning Lazily with K-Nearest Neighbors
   3. Leveraging the Correct k Parameter
   4. Implementing KNN Regression
   5. Implementing KNN Classification