更新时间：2021-07-23 17:12:06
封面
Title Page
Copyright and Credits
Building Machine Learning Systems with Python Third Edition
Packt Upsell
Why subscribe?
PacktPub.com
Contributors
About the authors
About the reviewers
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Getting Started with Python Machine Learning
Machine learning and Python – a dream team
What the book will teach you – and what it will not
How to best read this book
What to do when you are stuck
Getting started
Introduction to NumPy SciPy Matplotlib and TensorFlow
Installing Python
Chewing data efficiently with NumPy and intelligently with SciPy
Learning NumPy
Indexing
Handling nonexistent values
Comparing the runtime
Learning SciPy
Fundamentals of machine learning
Asking a question
Getting answers
Our first (tiny) application of machine learning
Reading in the data
Preprocessing and cleaning the data
Choosing the right model and learning algorithm
Before we build our first model
Starting with a simple straight line
Toward more complex models
Stepping back to go forward - another look at our data
Training and testing
Answering our initial question
Summary
Classifying with Real-World Examples
The Iris dataset
Visualization is a good first step
Classifying with scikit-learn
Building our first classification model
Evaluation – holding out data and cross-validation
How to measure and compare classifiers
A more complex dataset and the nearest-neighbor classifier
Learning about the seeds dataset
Features and feature engineering
Nearest neighbor classification
Looking at the decision boundaries
Which classifier to use
Regression
Predicting house prices with regression
Multidimensional regression
Cross-validation for regression
Penalized or regularized regression
L1 and L2 penalties
Using Lasso or ElasticNet in scikit-learn
Visualizing the Lasso path
P-greater-than-N scenarios
An example based on text documents
Setting hyperparameters in a principled way
Regression with TensorFlow
Classification I – Detecting Poor Answers
Sketching our roadmap
Learning to classify classy answers
Tuning the instance
Tuning the classifier
Fetching the data
Slimming the data down to chewable chunks
Preselecting and processing attributes
Defining what a good answer is
Creating our first classifier
Engineering the features
Training the classifier
Measuring the classifier's performance
Designing more features
Deciding how to improve the performance
Bias variance and their trade-off
Fixing high bias
Fixing high variance
High or low bias?
Using logistic regression
A bit of math with a small example
Applying logistic regression to our post-classification problem
Looking behind accuracy – precision and recall
Slimming the classifier
Ship it!
