使用R语言做机器学习的书籍推荐 / 开普饭 |
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总是有一些小伙伴觉得机器学习很高大上，令人望而生畏，其实它就是我们常见的统计学方法，比如做表达量矩阵分析，通常是需要绘制pca图看看组间差异是否足够明显。 如果你有单细胞转录组数据处理经验，实际上流程里面的降维聚类分群无一不是机器学习。如果你做肿瘤数据挖掘，经常会使用lasso，随机森林，支持向量机，它们都是在R里面非常容易实现。我们也多次推荐过 《精通机器学习:基于R(第2版)-图书-图灵社区》：https://www.ituring.com.cn/book/1989 （赠书活动） 我们还是有长期合作的出版社《图灵出版社》，他们会提供书籍作为粉丝礼物，老规矩（留言点赞前5名获得书籍），还有3个小要求： 必须有过赞赏记录 留言的点赞数量大于18 留言的是你与生信技能树的故事或者你为什么想要这本书 如果你不想看中文书籍有意思的是一些小伙伴对中文翻译比较抵触，喜欢看英文原版，我们也有推荐： 在线书籍地址：https://f0nzie.github.io/machine_learning_compilation/index.html 目录 1 Preface The Basics of Machine Learning 2 Introduction to PCA 3 Comparison of two PCA packages 4 Detailed study of Principal Component Analysis 5 Detection of diabetes using Logistic Regression 6 Sensitivity analysis for a neural network 7 Data Visualization for ML models Feature Engineering 8 Ten methods to assess Variable Importance 9 Employee Attrition using Feature Importance Classification 10 A gentle introduction to Support Vector Machines 11 Broad view of SVM 12 Feature Selection to enhance cancer detection 13 Dealing with unbalanced data 14 Imputting missing values with Random Forest 15 Tuning of Support Vector Machine prediction Classification 16 Introduction to algorithms for Classification 17 Comparing Classification algorithms 18 Who buys Social Network ads 19 Predicting Ozone levels 20 Building a Naive Bayes Classifier 21 Linear and Non-Linear Algorithms for Classification 22 Detect mines vs rocks with Random Forest 23 Predicting the type of glass 24 Naive Bayes for SMS spam 25 Vehicles classiification with Decision Trees 26 Applying Naive-Bayes on the Titanic case 27 Classification on bad loans 28 Predicting Flu outcome comparing eight classification algorithms 29 A detailed study of bike sharing demand 30 Prediction of arrhythmia with deep neural nets Linear Regression 31 Linear Regression with ISLR 32 Evaluation of three linear regression models 33 Comparison of six Linear Regression algorithms 34 Comparing regression models 35 Finding the factors of happiness 36 Regression with a neural network 37 Comparing Multiple Regression vs a Neural Network 38 Temperature modeling using nested dataframes Neural Networks 39 Credit Scoring with neuralnet 40 Wine classification with neuralnet 41 Predicting the rating of cereals 42 Fitting a linear model with neural networks 43 Visualization of neural networks 44 Build a fully connected R neural network from scratch 45 Tuning Hyperparameters in a Neural Network 46 Deep Learning tips for Classification and Regression Appendix A What is dot hat in a regression output B Q-Q normal to compare data to distributions C QQ and PP Plots D Visualizing residuals 书籍可能没有视频动画更加通俗易懂StatQuest生物统计学视频是一个很优秀的生物统计学教程，教程作者是Josh Starmer （个人博客 https://statquest.org/ ），生信菜鸟图很早之前就推过相关的学习资源。而且还组建过学习小分队，给视频写配套笔记： StatQuest生物统计学 - 二项分布 StatQuest生物统计学 - 中心极限定理 StatQuest生物统计学 - 箱线图 StatQuest生物统计学 - 二项分布的极大似然估计 StatQuest生物统计学 - 机器学习介绍 StatQuest生物统计学 - 机器学习之ConfusionMatrix StatQuest统计学视频列表更新 |
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