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  • Python知識分享網 - 專業的Python學習網站 學Python,上Python222
    Python機器學習 第2版 (影印版) PDF 下載
    發布于:2024-05-18 09:11:24
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    Python機器學習 第2版 (影印版) PDF 下載 圖1

     

     

    資料簡介:

    機器學習正在蠶食軟件世界。在這本Sebastian Raschka的暢銷書《Python機器學習(第二版)》中,你將了解并學習到機器學習、神經網絡和深度學習的 前沿知識。 塞巴斯蒂安·拉施卡、瓦希德·麥加利利著的《Python機器學習》 新并擴展了包括scikit-learn、Keras、TensorFlow在內的 開源技術。書中提供了使用Python創建有效的機器學習和深度學習應用所需的實用知識和技術。 在涉及數據分析的 主題之前,Sebastian Raschka和Vahid Mirjalili以其獨特見解和專業知識為你介紹機器學習和深度學習算法。本書將機器學習的理論原理與實際編碼方法相結合,以求全面掌握機器學習理論及其Python實現。

     

    資料目錄:

     

    Chapter 1: Giving Computers the Ability_ to Learn from Data
    Building intelligent machines to transform data into knowledge
    The three different types of machine learning
    Making predictions about the future with supervised learning
    Classification for predicting class labels
    Regression for predicting continuous outcomes
    Solving interactive problems with reinforcement learning
    Discovering hidden structures with unsupervised learning
    Finding subgroups with clustering
    Dimensionality reduction for data compression
    Introduction to the basic terminology and notations
    A roadmap for building machine learning systems
    Preprocessing - getting data into shape
    Training and selecting a predictive model
    Evaluating models and predicting unseen data instances
    Using Python for machine learning
    Installing Python and packages from the Python Package Index
    Using the Anaconda Python distribution and package manager
    Packages for scientific computing, data science, and machine learning
    Summary
    Chapter 2: Training Simple Machine Learning Algorithms
    for Classification
    Artificial neurons - a brief glimpse into the early history of
    machine learning
    The formal definition of an artificial neuron
    The perceptron learning rule
    Implementing a perceptron learning algorithm in Python
    An object-oriented perceptron API
    Training a perceptron model on the Iris dataset
    Adaptive linear neurons and the convergence of learning
    Minimizing cost functions with gradient descent
    Implementing Adaline in Python
    Improving gradient descent through feature scaling
    Large-scale machine learning and stochastic gradient descent
    Summary
    Chapter 3: A Tour of Machine Learning Classifiers
    Using scikit-learn
    Choosing a classification algorithm
    First steps with scikit-learn - training a perceptron
    Modeling class probabilities via logistic regression
    Logistic regression intuition and conditional probabilities
    Learning
    the weights of the logistic cost function
    Converting an Adaline implementation into an algorithm for
    logistic regression
    Training a logistic regression model with scikit-learn
    Tackling overfitting via regularization
    Maximum margin classification with support vector machines
    Maximum margin intuition
    Dealing with a nonlinearly separable case using slack variables
     

     

     

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