Hands-on Machine Learning with JavaScript

Table of contents

1. Title Page
2. Copyright and Credits
   1. Hands-On Machine Learning with JavaScript
3. Packt Upsell
   1. Why subscribe?
   2. PacktPub.com
4. Contributors
   1. About the author
   2. About the reviewer
   3. Packt is searching for authors like you
5. Preface
   1. Who this book is for
   2. What this book covers
   3. To get the most out of this book
      1. Download the example code files
      2. Download the color images
      3. Conventions used
   4. Get in touch
      1. Reviews
6. Exploring the Potential of JavaScript
   1. Why JavaScript?
   2. Why machine learning, why now?
   3. Advantages and challenges of JavaScript
   4. The CommonJS initiative
   5. Node.js
   6. TypeScript language
   7. Improvements in ES6
      1. Let and const
      2. Classes
      3. Module imports
      4. Arrow functions
      5. Object literals
      6. The for...of function
      7. Promises
      8. The async/await functions
   8. Preparing the development environment
      1. Installing Node.js
      2. Optionally installing Yarn
      3. Creating and initializing an example project
      4. Creating a Hello World project
   9. Summary
7. Data Exploration
   1. An overview
   2. Feature identification
      1. The curse of dimensionality
      2. Feature selection and feature extraction
      3. Pearson correlation example
   3. Cleaning and preparing data
      1. Handling missing data
         1. Missing categorical data
         2. Missing numerical data
      2. Handling noise
      3. Handling outliers
      4. Transforming and normalizing data
   4. Summary
8. Tour of Machine Learning Algorithms
   1. Introduction to machine learning
   2. Types of learning
      1. Unsupervised learning
      2. Supervised learning
         1. Measuring accuracy
         2. Supervised learning algorithms
      3. Reinforcement learning
   3. Categories of algorithms
      1. Clustering
      2. Classification
      3. Regression
      4. Dimensionality reduction
      5. Optimization
      6. Natural language processing
      7. Image processing
   4. Summary
9. Grouping with Clustering Algorithms
   1. Average and distance
   2. Writing the k-means algorithm
      1. Setting up the environment
      2. Initializing the algorithm
      3. Testing random centroid generation
      4. Assigning points to centroids
      5. Updating centroid locations
      6. The main loop
   3. Example 1 – k-means on simple 2D data
   4. Example 2 – 3D data
   5. k-means where k is unknown
   6. Summary
10. Classification Algorithms
    1. k-Nearest Neighbor
       1. Building the KNN algorithm
          1. Example 1 – Height, weight, and gender
          2. Example 2 – Decolorizing a photo
    2. Naive Bayes classifier
       1. Tokenization
       2. Building the algorithm
       3. Example 3 – Movie review sentiment
    3. Support Vector Machine
    4. Random forest
    5. Summary
11. Association Rule Algorithms
    1. The mathematical perspective
    2. The algorithmic perspective
    3. Association rule applications
    4. Example – retail data
    5. Summary
12. Forecasting with Regression Algorithms
    1. Regression versus classification
    2. Regression basics
    3. Example 1 – linear regression
    4. Example 2 – exponential regression
    5. Example 3 – polynomial regression
    6. Other time-series analysis techniques
       1. Filtering
       2. Seasonality analysis
       3. Fourier analysis
    7. Summary
13. Artificial Neural Network Algorithms
    1. Conceptual overview of neural networks
    2. Backpropagation training
    3. Example - XOR in TensorFlow.js
    4. Summary
14. Deep Neural Networks
    1. Convolutional Neural Networks
       1. Convolutions and convolution layers
       2. Example – MNIST handwritten digits
    2. Recurrent neural networks
       1. SimpleRNN
       2. Gated recurrent units
       3. Long Short-Term Memory
    3. Summary
15. Natural Language Processing in Practice
    1. String distance
    2. Term frequency - inverse document frequency
    3. Tokenizing
    4. Stemming
    5. Phonetics
    6. Part of speech tagging
    7. Word embedding and neural networks
    8. Summary
16. Using Machine Learning in Real-Time Applications
    1. Serializing models
       1. Training models on the server
       2. Web workers
       3. Continually improving and per-user models
    2. Data pipelines
       1. Data querying
       2. Data joining and aggregation
       3. Transformation and normalization
       4. Storing and delivering data
    3. Summary
17. Choosing the Best Algorithm for Your Application
    1. Mode of learning
    2. The task at hand
    3. Format, form, input, and output
    4. Available resources
    5. When it goes wrong
    6. Combining models
    7. Summary
18. Other Books You May Enjoy
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