更新时间：2021-07-02 19:01:34
coverpage
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Why subscribe?
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
The Fundamentals of Machine Learning
Defining machine learning
Learning from experience
Machine learning tasks
Training data testing data and validation data
Bias and variance
An introduction to scikit-learn
Installing scikit-learn
Installing using pip
Installing on Windows
Installing on Ubuntu 16.04
Installing on Mac OS
Installing Anaconda
Verifying the installation
Installing pandas Pillow NLTK and matplotlib
Summary
Simple Linear Regression
Simple linear regression
Evaluating the fitness of the model with a cost function
Solving OLS for simple linear regression
Evaluating the model
Classification and Regression with k-Nearest Neighbors
K-Nearest Neighbors
Lazy learning and non-parametric models
Classification with KNN
Regression with KNN
Scaling features
Feature Extraction
Extracting features from categorical variables
Standardizing features
Extracting features from text
The bag-of-words model
Stop word filtering
Stemming and lemmatization
Extending bag-of-words with tf-idf weights
Space-efficient feature vectorizing with the hashing trick
Word embeddings
Extracting features from images
Extracting features from pixel intensities
Using convolutional neural network activations as features
From Simple Linear Regression to Multiple Linear Regression
Multiple linear regression
Polynomial regression
Regularization
Applying linear regression
Exploring the data
Fitting and evaluating the model
Gradient descent
From Linear Regression to Logistic Regression
Binary classification with logistic regression
Spam filtering
Binary classification performance metrics
Accuracy
Precision and recall
Calculating the F1 measure
ROC AUC
Tuning models with grid search
Multi-class classification
Multi-class classification performance metrics
Multi-label classification and problem transformation
Multi-label classification performance metrics
Naive Bayes
Bayes' theorem
Generative and discriminative models
Assumptions of Naive Bayes
Naive Bayes with scikit-learn
Nonlinear Classification and Regression with Decision Trees
Decision trees
Training decision trees
Selecting the questions
Information gain
Gini impurity
Decision trees with scikit-learn
Advantages and disadvantages of decision trees
