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相关截图Q?br />  资料介：(x) 机器学习(fn)正在蚕食软g世界。在q本Sebastian Raschka的畅销书《Python机器学习(fn)Q第二版Q》中Q你了解ƈ学习(fn)到机器学?fn)、神l网l和深度学习(fn)?前沿知识?塞巴斯蒂?middot;拉施卡、瓦希d·麦加利利著的《Python机器学习(fn)?新ƈ扩展了包括scikit-learn、Keras、TensorFlow在内?开源技术。书中提供了使用Python创徏有效的机器学?fn)和深度学?fn)应用所需的实用知识和技术?在涉?qing)数据分析?主题之前QSebastian Raschka和Vahid Mirjalili以其独特见解和专业知识ؓ(f)你介l机器学?fn)和深度学?fn)法。本书将机器学习(fn)的理论原理与实际~码Ҏ(gu)相结合，以求全面掌握机器学习(fn)理论?qing)其Python实现?/span> 资料目录Q?br /> 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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