Mastering Machine Learning with scikit-learn, 2/e (Paperback)

Gavin Hackeling

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商品描述

Key Features

  • 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

Book Description

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.

What you will learn

  • 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 im4:22 PM 8/2/2017ages using logistic regression and support

商品描述(中文翻譯)

主要特點


  • 掌握流行的機器學習模型,包括k最近鄰、隨機森林、邏輯回歸、k均值、朴素貝葉斯和人工神經網絡

  • 學習如何使用scikit-learn構建和評估高效模型

  • 實用指南,從真實應用中掌握基礎並學習機器學習

書籍描述

機器學習是一個將計算機科學和統計學結合起來構建智能高效模型的熱門詞彙。使用機器學習提供的強大算法和技術,您可以自動化任何分析模型。

本書探討了各種機器學習模型,包括流行的機器學習算法,如k最近鄰、邏輯回歸、朴素貝葉斯、k均值、決策樹和人工神經網絡。它討論了數據預處理、超參數優化和集成方法。您將構建能夠對文檔進行分類、識別圖像、檢測廣告等系統。您將學習使用scikit-learn的API從分類變量、文本和圖像中提取特徵;評估模型性能,並培養改進模型性能的直覺。

通過閱讀本書,您將掌握scikit-learn的所有必要概念,以便在工作中構建高效模型,執行高級任務。

您將學到什麼


  • 回顧基本概念,如偏差和方差

  • 從分類變量、文本和圖像中提取特徵

  • 使用線性回歸和K最近鄰預測連續變量的值

  • 使用邏輯回歸和支持向量機分類文檔和圖像