Ordinary linear regression assumes that the response variable is normally distributed. Normal distribution, or Gaussian distribution, is a function that describes the probability that an observation will have a value between any two real numbers. Normally distributed data is symmetrical; half of the values are greater than the mean and half of the values are less than the mean. The mean, median, and mode of normally distributed data are also equal. Many natural phenomena are approximately normally distributed. For instance, the height of people is normally distributed: most people are of average height, a few are tall, and a few are short. In some problems the response variable is not normally distributed. For instance, a coin toss can result in two outcomes: heads or tails. Bernoulli distribution describes the probability distribution...
-
Book Overview & Buying
-
Table Of Contents
-
Feedback & Rating
Mastering Machine Learning with scikit-learn
By :
Mastering Machine Learning with scikit-learn
By:
Overview of this book
Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model.
This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance.
By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.
Table of Contents (15 chapters)
Preface
Free Chapter
The Fundamentals of Machine Learning
Simple Linear Regression
Classification and Regression with k-Nearest Neighbors
Feature Extraction
From Simple Linear Regression to Multiple Linear Regression
From Linear Regression to Logistic Regression
Naive Bayes
Nonlinear Classification and Regression with Decision Trees
From Decision Trees to Random Forests and Other Ensemble Methods
The Perceptron
From the Perceptron to Support Vector Machines
From the Perceptron to Artificial Neural Networks
K-means
Dimensionality Reduction with Principal Component Analysis
Customer Reviews
