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Mastering Machine Learning with scikit-learn - Second Edition

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  • Review fundamental concepts such as bias and variance
  • Extract features from categorical variables, text, and images
  • Predict the values of continuous variables using linear regression and K Nearest Neighbors
  • Classify documents and images using logistic regression and support vector machines
  • Create ensembles of estimators using bagging and boosting techniques
  • Discover hidden structures in data using K-Means clustering
  • Evaluate the performance of machine learning systems in common tasks

Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model.

This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance.

By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.

  • Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks
  • Learn how to build and evaluate performance of efficient models using scikit-learn
  • Practical guide to master your basics and learn from real life applications of machine learning
Page Count 254
Course Length 7 hours 37 minutes
Date Of Publication 23 Jul 2017


Gavin Hackeling

Gavin Hackeling develops machine learning services for large-scale documents and image classification at an advertising network in New York. He received his Master's degree from New York University's Interactive Telecommunications Program, and his Bachelor's degree from the University of North Carolina.