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）

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

Tuning a Random Forest-checkpoint.ipynb 41KB

Create a Simple Estimator.ipynb 11KB

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

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

Tuning a Decision Tree-checkpoint.ipynb 32KB

Decomposition to classify with DictionaryLearning.ipynb 92KB

Using KMeans to cluster data.ipynb 104KB

Regression Metrics-checkpoint.ipynb 7KB

dtree.clf 3KB

Create a Simple Estimator-checkpoint.ipynb 11KB

Visualize a Decision Tree with pydot.ipynb 125KB

Randomized Search with scikit-learn.ipynb 8KB

Using truncated SVD to reduce dimensionality.ipynb 12KB

Stacking with a Neural Network-checkpoint.ipynb 20KB

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

Imputing missing values through various strategies.ipynb 7KB

Optimizing the ridge regression parameter.ipynb 43KB

Varying the Classification Threshold in Logistic Regression.ipynb 46KB

Fitting a line through data-checkpoint.ipynb 13KB

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

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

Quantizing an image with KMeans clustering.ipynb 277KB

Random Forest Regression-checkpoint.ipynb 23KB

ROC Analysis.ipynb 16KB

Machine Learning with Logistic Regression.ipynb 7KB

Creating sample data for toy analysis.ipynb 22KB

Doing basic classifications with Decision Trees.ipynb 62KB

Tuning a Random Forest.ipynb 41KB

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

Using Gaussian processes for regression.ipynb 128KB

Pipelines Testing Methods to Reduce Dimensionality.ipynb 95KB

Decision Trees for Regression-checkpoint.ipynb 35KB

Dimensionality Reduction with Manifolds tSNE-checkpoint.ipynb 148KB

Tuning a Decision Tree.ipynb 893KB

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

Feature selection.ipynb 11KB

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

Reducing dimensionality with PCA-checkpoint.ipynb 181KB

Regression Metrics.ipynb 7KB

Tuning Gradient Boosting Trees.ipynb 35KB

Numpy Basics.ipynb 11KB

Cross-validation with ShuffleSplit.ipynb 32KB

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

Viewing the Iris Dataset with Pandas.ipynb 21KB

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

Optimizing the number of centroids.ipynb 20KB

Optimizing the number of centroids-checkpoint.ipynb 20KB

Random Forest Regression.ipynb 23KB

Scaling data to the standard normal.ipynb 25KB

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

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

Stacking with a Neural Network.ipynb 20KB

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

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

Using stochastic gradient descent for regression.ipynb 11KB

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

ROC Analysis-checkpoint.ipynb 22KB

Using KMeans to cluster data-checkpoint.ipynb 104KB

Viewing the Iris Dataset-checkpoint.ipynb 11KB

Classification Metrics.ipynb 9KB

Using KMeans for outlier detection.ipynb 60KB

Using k-NN for regression.ipynb 113KB

Working with categorical variables-checkpoint.ipynb 7KB

Multiclass SVC Classifier.ipynb 664KB

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

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

Evaluating the linear regression model.ipynb 36KB

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

Decomposition to classify with DictionaryLearning-checkpoint.ipynb 92KB

Assessing cluster correctness.ipynb 93KB

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

Plotting with Numpy and Matplotlib-checkpoint.ipynb 108KB

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

Using LDA for classification.ipynb 58KB

Perceptron Classifier.ipynb 9KB

Plotting an ROC Curve without Context.ipynb 8KB

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

Viewing the Iris Dataset.ipynb 11KB

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

Reducing dimensionality with PCA.ipynb 181KB

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

Dimensionality Reduction with Manifolds tSNE.ipynb 148KB

Using KMeans for outlier detection-checkpoint.ipynb 60KB

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

Plotting with Numpy and Matplotlib.ipynb 108KB

Probabilistic clustering with Gaussian Mixture Models.ipynb 70KB

Decision Trees for Regression.ipynb 35KB

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

Perceptron Classifier-checkpoint.ipynb 9KB

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

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

Classifying documents with Naive Bayes.ipynb 8KB

Classification Metrics-checkpoint.ipynb 9KB

Assessing cluster correctness-checkpoint.ipynb 93KB

Working with categorical variables.ipynb 7KB

Optimizing a Support Vector Machine-checkpoint.ipynb 204KB

Using Gaussian processes for regression-checkpoint.ipynb 128KB

Fitting a line through data.ipynb 13KB

Optimizing a Support Vector Machine.ipynb 204KB

共 182 条

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