掌握scikit-learn机器学习：《scikit-learn Cookbook第二版》深度解读

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资源摘要信息:"scikit-learn-Cookbook-Second-Edition:Packt发行的scikit-learn Cookbook第二版" 知识点： 1. Python在数据分析和机器学习中的应用：本书通过scikit-learn的实践案例，展示了Python语言在数据分析和机器学习领域的强大能力。Python的简单性和灵活性使其成为了数据科学家和分析师的首选语言。 2. scikit-learn的介绍和应用：scikit-learn是Python中用于机器学习的一个库，它提供了许多用于数据挖掘和数据分析的工具。本书将详细介绍如何使用scikit-learn进行各种机器学习任务。 3. 机器学习的基本概念：本书将介绍机器学习中的常见问题和不常见问题，包括数据预处理，线性回归，逻辑回归，K-NN，朴素贝叶斯，分类，决策树，合奏等。 4. 数据预处理：在进行机器学习之前，需要对数据进行预处理，包括数据清洗，数据转换，数据规约等。本书将详细介绍如何使用scikit-learn进行数据预处理。 5. 线性回归和逻辑回归：线性回归和逻辑回归是两种基本的回归分析方法。本书将介绍如何使用scikit-learn实现线性回归和逻辑回归。 6. K-NN和朴素贝叶斯：K-NN和朴素贝叶斯是两种基本的分类算法。本书将介绍如何使用scikit-learn实现K-NN和朴素贝叶斯。 7. 分类和决策树：分类是将数据分为多个类别，决策树是一种常用的分类算法。本书将介绍如何使用scikit-learn实现分类和决策树。 8. 合奏方法：合奏方法是一种集成学习方法，包括随机森林，提升树等。本书将介绍如何使用scikit-learn实现合奏方法。 9. 模型优化：包括多类分类，交叉验证，模型评估等。本书将介绍如何使用scikit-learn进行模型优化。 10. scikit-learn中的深度学习：除了传统的机器学习方法，scikit-learn还支持深度学习。本书将介绍如何使用scikit-learn进行深度学习。 11. scikit-learn的API和分类器，回归器和估计器：scikit-learn提供了一套丰富的API和工具，包括分类器，回归器和估计器等。本书将详细介绍如何使用这些工具。 12. JupyterNotebook的使用：JupyterNotebook是一个开源的Web应用程序，允许创建和共享包含代码，可视化和解释文本的文档。本书的代码示例都在JupyterNotebook中实现。以上就是对scikit-learn-Cookbook-Second-Edition:Packt发行的scikit-learn Cookbook第二版的详细知识点总结。

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scikit-learn-Cookbook-Second-Edition:Packt发行的scikit-learn Cookbook第二版（182个子文件）

Pipelines Testing Methods to Reduce Dimensionality-checkpoint.ipynb 95KB

Varying the Classification Threshold in Logistic Regression.ipynb 46KB

Using KMeans for outlier detection.ipynb 60KB

Tuning a Decision Tree.ipynb 893KB

Stacking with a Neural Network.ipynb 20KB

Evaluating the linear regression model.ipynb 36KB

Reducing dimensionality with PCA.ipynb 181KB

Numpy Basics.ipynb 11KB

Using k-NN for regression-checkpoint.ipynb 113KB

Decomposition to classify with DictionaryLearning-checkpoint.ipynb 92KB

Plotting an ROC Curve without Context-checkpoint.ipynb 8KB

Quantizing an image with KMeans clustering-checkpoint.ipynb 277KB

Creating sample data for toy analysis.ipynb 22KB

Putting it All Together UCI Breast Cancer Data Set.ipynb 15KB

Using ridge regression to overcome linear regression's shortfalls-checkpoint.ipynb 41KB

Classification Metrics.ipynb 9KB

Using k-NN for regression.ipynb 113KB

Using truncated SVD to reduce dimensionality.ipynb 12KB

Using KMeans to cluster data-checkpoint.ipynb 104KB

A linear model in the presence of outliers.ipynb 55KB

Machine Learning with Logistic Regression.ipynb 7KB

Finding the closest object in the feature space-checkpoint.ipynb 25KB

Writing+A+Stacking+Agreggator+with+Scikit-Learn+Extra(2).html 285KB

Classification Metrics-checkpoint.ipynb 9KB

A linear model in the presence of outliers-checkpoint.ipynb 55KB

Regression Metrics.ipynb 7KB

Create a Simple Estimator.ipynb 11KB

Optimizing the ridge regression parameter.ipynb 43KB

Fitting a line through data with Machine Learning.ipynb 10KB

Imputing missing values through various strategies.ipynb 7KB

Optimizing the number of centroids.ipynb 20KB

Multiclass SVC Classifier.ipynb 664KB

Writing A Stacking Agreggator with Scikit-Learn-checkpoint.ipynb 27KB

Quantizing an image with KMeans clustering.ipynb 277KB

Writing A Stacking Agreggator with Scikit-Learn.ipynb 27KB

Dimensionality Reduction with Manifolds tSNE.ipynb 148KB

Visualize a Decision Tree with pydot-checkpoint.ipynb 125KB

Using KMeans to cluster data.ipynb 104KB

Classifying data with a Linear Support Vector Machines-checkpoint.ipynb 191KB

Working with categorical variables.ipynb 7KB

Tuning a Decision Tree-checkpoint.ipynb 32KB

Doing basic classifications with Decision Trees.ipynb 62KB

ROC Analysis-checkpoint.ipynb 22KB

Decision Trees for Regression-checkpoint.ipynb 35KB

Putting it All Together UCI Breast Cancer Data Set-checkpoint.ipynb 15KB

Using Gaussian processes for regression-checkpoint.ipynb 128KB

Viewing the Iris Dataset with Pandas-checkpoint.ipynb 22KB

Probabilistic clustering with Gaussian Mixture Models.ipynb 70KB

Plotting with Numpy and Matplotlib-checkpoint.ipynb 108KB

Using stochastic gradient descent for regression.ipynb 11KB

Classifying data with a Linear Support Vector Machines.ipynb 191KB

Classifying documents with Naive Bayes.ipynb 8KB

Viewing the Iris Dataset with Pandas.ipynb 21KB

Perceptron Classifier.ipynb 9KB

Randomized Search with scikit-learn-checkpoint.ipynb 8KB

Using Gaussian processes for regression.ipynb 128KB

Plotting an ROC Curve without Context.ipynb 8KB

Reducing dimensionality with PCA-checkpoint.ipynb 181KB

Viewing the Pima Indians Diabetes Dataset with Pandas.ipynb 76KB

Using KMeans for outlier detection-checkpoint.ipynb 60KB

Random Forest Regression.ipynb 23KB

Using LDA for classification.ipynb 58KB

Finding the closest object in the feature space.ipynb 25KB

Create a Simple Estimator-checkpoint.ipynb 11KB

Using ridge regression to overcome linear regression's shortfalls.ipynb 41KB

Pipelines Testing Methods to Reduce Dimensionality.ipynb 95KB

Feature selection.ipynb 11KB

Fitting a line through data with Machine Learning-checkpoint.ipynb 10KB

dtree.clf 3KB

Viewing the Iris Dataset.ipynb 11KB

Plotting with Numpy and Matplotlib.ipynb 108KB

Working with categorical variables-checkpoint.ipynb 7KB

Decision Trees for Regression.ipynb 35KB

Assessing cluster correctness-checkpoint.ipynb 93KB

Taking a more fundamental approach to regularization with LARS.ipynb 76KB

Randomized Search with scikit-learn.ipynb 8KB

Random Forest Regression-checkpoint.ipynb 23KB

Visualize a Decision Tree with pydot.ipynb 125KB

Stacking with a Neural Network-checkpoint.ipynb 20KB

Fitting a line through data-checkpoint.ipynb 13KB

Dimensionality Reduction with Manifolds tSNE-checkpoint.ipynb 148KB

Regression Metrics-checkpoint.ipynb 7KB

Perceptron Classifier-checkpoint.ipynb 9KB

Taking a more fundamental approach to regularization with LARS-checkpoint.ipynb 76KB

Assessing cluster correctness.ipynb 93KB

Viewing the Iris Dataset-checkpoint.ipynb 11KB

ROC Analysis.ipynb 16KB

Cross-validation with ShuffleSplit.ipynb 32KB

Writing A Stacking Agreggator with Scikit-Learn Extra.ipynb 20KB

Tuning a Random Forest-checkpoint.ipynb 41KB

Imputing missing values through various strategies-checkpoint.ipynb 7KB

Optimizing a Support Vector Machine-checkpoint.ipynb 204KB

Optimizing the number of centroids-checkpoint.ipynb 20KB

Fitting a line through data.ipynb 13KB

Scaling data to the standard normal.ipynb 25KB

Optimizing a Support Vector Machine.ipynb 204KB

Viewing the Pima Indians Diabetes Dataset with Pandas-checkpoint.ipynb 76KB

Tuning a Random Forest.ipynb 41KB

Decomposition to classify with DictionaryLearning.ipynb 92KB

Tuning Gradient Boosting Trees.ipynb 35KB

共 182 条

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掌握scikit-learn机器学习：《scikit-learn Cookbook第二版》深度解读

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