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  • Title Page
  • Copyright and Credits
    • Hands-On Automated Machine Learning
  • 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
  • Introduction to AutoML
    • Scope of machine learning
    • What is AutoML?
    • Why use AutoML and how does it help?
    • When do you automate ML?
    • What will you learn?
      • Core components of AutoML systems
        • Automated feature preprocessing
        • Automated algorithm selection
        • Hyperparameter optimization
      • Building prototype subsystems for each component
      • Putting it all together as an end–to–end AutoML system
    • Overview of AutoML libraries
      • Featuretools
      • Auto-sklearn
      • MLBox
      • TPOT
    • Summary
  • Introduction to Machine Learning Using Python
    • Technical requirements
    • Machine learning
      • Machine learning process
      • Supervised learning
      • Unsupervised learning
    • Linear regression
      • What is linear regression?
        • Working of OLS regression
        • Assumptions of OLS
      • Where is linear regression used?
      • By which method can linear regression be implemented?
    • Important evaluation metrics – regression algorithms
    • Logistic regression
      • What is logistic regression?
      • Where is logistic regression used?
      • By which method can logistic regression be implemented?
    • Important evaluation metrics – classification algorithms
    • Decision trees
      • What are decision trees?
      • Where are decision trees used?
      • By which method can decision trees be implemented?
    • Support Vector Machines
      • What is SVM?
      • Where is SVM used?
      • By which method can SVM be implemented?
    • k-Nearest Neighbors
      • What is k-Nearest Neighbors?
      • Where is KNN used?
      • By which method can KNN be implemented?
    • Ensemble methods
      • What are ensemble models?
        • Bagging
        • Boosting
        • Stacking/blending
    • Comparing the results of classifiers
    • Cross-validation
    • Clustering
      • What is clustering?
      • Where is clustering used?
      • By which method can clustering be implemented?
      • Hierarchical clustering
      • Partitioning clustering (KMeans)
    • Summary
  • Data Preprocessing
    • Technical requirements
    • Data transformation
      • Numerical data transformation
        • Scaling
        • Missing values
        • Outliers
          • Detecting and treating univariate outliers
          • Inter-quartile range
          • Filtering values
          • Winsorizing
          • Trimming
          • Detecting and treating multivariate outliers
        • Binning
        • Log and power transformations
      • Categorical data transformation
        • Encoding
        • Missing values for categorical data transformation
      • Text preprocessing
    • Feature selection
      • Excluding features with low variance
      • Univariate feature selection
      • Recursive feature elimination
      • Feature selection using random forest
      • Feature selection using dimensionality reduction
        • Principal Component Analysis
    • Feature generation
    • Summary
  • Automated Algorithm Selection
    • Technical requirements
    • Computational complexity
      • Big O notation
    • Differences in training and scoring time
      • Simple measure of training and scoring time 
      • Code profiling in Python
      • Visualizing performance statistics
      • Implementing k-NN from scratch
      • Profiling your Python script line by line
    • Linearity versus non-linearity
      • Drawing decision boundaries
      • Decision boundary of logistic regression
      • The decision boundary of random forest
      • Commonly used machine learning algorithms
    • Necessary feature transformations
    • Supervised ML
      • Default configuration of auto-sklearn
      • Finding the best ML pipeline for product line prediction
      • Finding the best machine learning pipeline for network anomaly detection
    • Unsupervised AutoML
      • Commonly used clustering algorithms
      • Creating sample datasets with sklearn
      • K-means algorithm in action
      • The DBSCAN algorithm in action
      • Agglomerative clustering algorithm in action
      • Simple automation of unsupervised learning
      • Visualizing high-dimensional datasets
      • Principal Component Analysis in action
      • t-SNE in action
      • Adding simple components together to improve the pipeline
    • Summary
  • Hyperparameter Optimization
    • Technical requirements
    • Hyperparameters
    • Warm start
    • Bayesian-based hyperparameter tuning
    • An example system
    • Summary
  • Creating AutoML Pipelines
    • Technical requirements
    • An introduction to machine learning pipelines
    • A simple pipeline
    • FunctionTransformer
    • A complex pipeline
    • Summary
  • Dive into Deep Learning
    • Technical requirements
    • Overview of neural networks
      • Neuron
      • Activation functions
        • The step function
        • The sigmoid function
        • The ReLU function
        • The tanh function
    • A feed-forward neural network using Keras
    • Autoencoders
    • Convolutional Neural Networks
      • Why CNN?
      • What is convolution?
      • What are filters?
      • The convolution layer
      • The ReLU layer
      • The pooling layer
      • The fully connected layer
    • Summary
  • Critical Aspects of ML and Data Science Projects
    • Machine learning as a search
    • Trade-offs in machine learning
    • Engagement model for a typical data science project
    • The phases of an engagement model
      • Business understanding
      • Data understanding
      • Data preparation
      • Modeling
      • Evaluation
      • Deployment
    • Summary
  • Other Books You May Enjoy
    • Leave a review - let other readers know what you think
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