更新时间：2021-07-02 15:47:29
coverpage
Title Page
Copyright
Machine Learning for Developers
Credits
Foreword
About the Author
About the Reviewers
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
Introduction - Machine Learning and Statistical Science
Machine learning in the bigger picture
Types of machine learning
Grades of supervision
Supervised learning strategies - regression versus classification
Unsupervised problem solving–clustering
Tools of the trade–programming language and libraries
The Python language
The NumPy library
The matplotlib library
What's matplotlib?
Pandas
SciPy
Jupyter notebook
Basic mathematical concepts
Statistics - the basic pillar of modeling uncertainty
Descriptive statistics - main operations
Mean
Variance
Standard deviation
Probability and random variables
Events
Probability
Random variables and distributions
Useful probability distributions
Bernoulli distributions
Uniform distribution
Normal distribution
Logistic distribution
Statistical measures for probability functions
Skewness
Kurtosis
Differential calculus elements
Preliminary knowledge
In search of changes–derivatives
Sliding on the slope
Chain rule
Partial derivatives
Summary
The Learning Process
Understanding the problem
Dataset definition and retrieval
The ETL process
Loading datasets and doing exploratory analysis with SciPy and pandas
Working interactively with IPython
Working on 2D data
Feature engineering
Imputation of missing data
One hot encoding
Dataset preprocessing
Normalization and feature scaling
Normalization or standardization
Model definition
Asking ourselves the right questions
Loss function definition
Model fitting and evaluation
Dataset partitioning
Common training terms – iteration batch and epoch
Types of training – online and batch processing
Parameter initialization
Model implementation and results interpretation
Regression metrics
Mean absolute error
Median absolute error
Mean squared error
Classification metrics
Accuracy
Precision score recall and F-measure
Confusion matrix
Clustering quality measurements
Silhouette coefficient
Homogeneity completeness and V-measure
References
Clustering
Grouping as a human activity
Automating the clustering process
Finding a common center - K-means
