Machine Learning Quick Reference

Machine Learning Quick Reference

by Rahul Kumar
Released January 2019
Publisher(s): Packt Publishing
ISBN: 9781788830577

Read it now on the O’Reilly learning platform with a 10-day free trial.

O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Start your free trial

Book description

Your hands-on reference guide to developing, training, and optimizing your machine learning models

Key Features

  • Your guide to learning efficient machine learning processes from scratch
  • Explore expert techniques and hacks for a variety of machine learning concepts
  • Write effective code in R, Python, Scala, and Spark to solve all your machine learning problems

Book Description

Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner.

After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered.

By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference.

What you will learn

  • Get a quick rundown of model selection, statistical modeling, and cross-validation
  • Choose the best machine learning algorithm to solve your problem
  • Explore kernel learning, neural networks, and time-series analysis
  • Train deep learning models and optimize them for maximum performance
  • Briefly cover Bayesian techniques and sentiment analysis in your NLP solution
  • Implement probabilistic graphical models and causal inferences
  • Measure and optimize the performance of your machine learning models

Who this book is for

If you're a machine learning practitioner, data scientist, machine learning developer, or engineer, this book will serve as a reference point in building machine learning solutions. You will also find this book useful if you're an intermediate machine learning developer or data scientist looking for a quick, handy reference to all the concepts of machine learning. You'll need some exposure to machine learning to get the best out of this book.

Table of contents

  1. Title Page
  2. Copyright and Credits
    1. Machine Learning Quick Reference
  3. About Packt
    1. Why subscribe?
    2. Packt.com
  4. Contributors
    1. About the author
    2. About the reviewers
    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. Quantifying Learning Algorithms
    1. Statistical models
    2. Learning curve
      1. Machine learning
      2. Wright's model
    3. Curve fitting
      1. Residual
    4. Statistical modeling – the two cultures of Leo Breiman
    5. Training data development data – test data
      1. Size of the training, development, and test set
    6. Bias-variance trade off
    7. Regularization
      1. Ridge regression (L2)
      2. Least absolute shrinkage and selection operator 
    8. Cross-validation and model selection
      1. K-fold cross-validation
    9. Model selection using cross-validation
    10. 0.632 rule in bootstrapping
    11. Model evaluation
      1. Confusion matrix
    12. Receiver operating characteristic curve
      1. Area under ROC
    13. H-measure
    14. Dimensionality reduction
    15. Summary
  7. Evaluating Kernel Learning
    1. Introduction to vectors
      1. Magnitude of the vector
      2. Dot product
    2. Linear separability
    3. Hyperplanes 
    4. SVM
      1. Support vector
    5. Kernel trick
      1. Kernel
      2. Back to Kernel trick
    6. Kernel types
      1. Linear kernel
      2. Polynomial kernel
      3. Gaussian kernel
    7. SVM example and parameter optimization through grid search
    8. Summary
  8. Performance in Ensemble Learning
    1. What is ensemble learning?
      1. Ensemble methods 
        1. Bootstrapping
    2. Bagging
    3. Decision tree
      1. Tree splitting
      2. Parameters of tree splitting
    4. Random forest algorithm
      1. Case study
    5. Boosting
      1. Gradient boosting
        1. Parameters of gradient boosting
    6. Summary
  9. Training Neural Networks
    1. Neural networks
      1. How a neural network works
      2. Model initialization
      3. Loss function
      4. Optimization
      5. Computation in neural networks
        1. Calculation of activation for H1
      6. Backward propagation
      7. Activation function
        1. Types of activation functions
    2. Network initialization
      1. Backpropagation
    3. Overfitting
    4. Prevention of overfitting in NNs
    5. Vanishing gradient 
      1. Overcoming vanishing gradient
    6. Recurrent neural networks
      1. Limitations of RNNs
      2. Use case
    7. Summary
  10. Time Series Analysis
    1. Introduction to time series analysis
    2. White noise
      1. Detection of white noise in a series
    3. Random walk
    4. Autoregression
    5. Autocorrelation
    6. Stationarity
      1. Detection of stationarity
    7. AR model
    8. Moving average model
    9. Autoregressive integrated moving average
    10. Optimization of parameters
      1. AR model
      2. ARIMA model
    11. Anomaly detection
    12. Summary
  11. Natural Language Processing
    1. Text corpus
      1. Sentences
      2. Words
        1. Bags of words
    2. TF-IDF
      1. Executing the count vectorizer
      2. Executing TF-IDF in Python
    3. Sentiment analysis
      1. Sentiment classification
        1. TF-IDF feature extraction
        2. Count vectorizer bag of words feature extraction
          1. Model building count vectorization
    4. Topic modeling 
      1. LDA architecture
      2. Evaluating the model
      3. Visualizing the LDA
      4. The Naive Bayes technique in text classification
    5. The Bayes theorem
      1. How the Naive Bayes classifier works
    6. Summary
  12. Temporal and Sequential Pattern Discovery
    1. Association rules
    2. Apriori algorithm
      1. Finding association rules
    3. Frequent pattern growth
      1. Frequent pattern tree growth
      2. Validation 
        1. Importing the library
    4. Summary
  13. Probabilistic Graphical Models
    1. Key concepts
    2. Bayes rule
    3. Bayes network
      1. Probabilities of nodes
      2. CPT
      3. Example of the training and test set
    4. Summary
  14. Selected Topics in Deep Learning
    1. Deep neural networks
      1. Why do we need a deep learning model?
      2. Deep neural network notation
      3. Forward propagation in a deep network
      4. Parameters W and b
      5. Forward and backward propagation
      6. Error computation
    2. Backward propagation
    3. Forward propagation equation
    4. Backward propagation equation
    5. Parameters and hyperparameters
    6. Bias initialization
      1. Hyperparameters
      2. Use case – digit recognizer
    7. Generative adversarial networks
    8. Hinton's Capsule network
      1. The Capsule Network and convolutional neural networks
    9. Summary
  15. Causal Inference
    1. Granger causality
    2. F-test
      1. Limitations
      2. Use case
    3. Graphical causal models
    4. Summary
  16. Advanced Methods
    1. Introduction
    2. Kernel PCA
    3. Independent component analysis
      1. Preprocessing for ICA
      2. Approach
    4. Compressed sensing
      1. Our goal
    5. Self-organizing maps
      1. SOM
    6. Bayesian multiple imputation
    7. Summary
  17. Other Books You May Enjoy
    1. Leave a review - let other readers know what you think

Product information

  • Title: Machine Learning Quick Reference
  • Author(s): Rahul Kumar
  • Release date: January 2019
  • Publisher(s): Packt Publishing
  • ISBN: 9781788830577