更新时间：2021-06-24 18:55:58
coverpage
Title Page
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Contributors
About the author
About the reviewers
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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 Machine Learning
What is AI?
The motivation behind ML
What is ML ?
Applications of ML
Digital signal processing (DSP)
Computer vision
Natural language processing (NLP)
Other applications of ML
Using ML to build smarter iOS applications
Getting to know your data
Features
Types of features
Choosing a good set of features
Getting the dataset
Data preprocessing
Choosing a model
Types of ML algorithms
Supervised learning
Unsupervised learning
Reinforcement learning
Mathematical optimization – how learning works
Mobile versus server-side ML
Understanding mobile platform limitations
Summary
Bibliography
Classification – Decision Tree Learning
Machine learning toolbox
Prototyping the first machine learning app
Tools
Setting up a machine learning environment
IPython notebook crash course
Time to practice
Machine learning for extra-terrestrial life explorers
Loading the dataset
Exploratory data analysis
Converting categorical variables
Separating features from labels
One-hot encoding
Splitting the data
Decision trees everywhere
Training the decision tree classifier
Tree visualization
Making predictions
Evaluating accuracy
Tuning hyperparameters
Understanding model capacity trade-offs
How decision tree learning works
Building a tree automatically from data
Combinatorial entropy
Evaluating performance of the model with data
Precision recall and F1-score
K-fold cross-validation
Confusion matrix
Implementing first machine learning app in Swift
Introducing Core ML
Core ML features
Exporting the model for iOS
Ensemble learning random forest
Training the random forest
Random forest accuracy evaluation
Importing the Core ML model into an iOS project
Evaluating performance of the model on iOS
Calculating the confusion matrix
Decision tree learning pros and cons
K-Nearest Neighbors Classifier
Calculating the distance
DTW
Implementing DTW in Swift
Using instance-based models for classification and clustering
People motion recognition using inertial sensors
Understanding the KNN algorithm
Implementing KNN in Swift
Recognizing human motion using KNN
Cold start problem
Balanced dataset
Choosing a good k
Reasoning in high-dimensional spaces
KNN pros
KNN cons
