Machine_Learning_Mastery_With_Python_-_Understand_Your_Data,_Create_Accurate_Models_and_Work_Projects_End-To-End.pdf ....pdf

所需积分/C币:9 2019-09-16 04:00:15 1.97MB PDF

Machine_Learning_Mastery_With_Python_-_Understand_Your_Data,_Create_Accurate_Models_and_Work_Projects_End-To-End.pdf
Machine Learning Mastery with Python C Copyright 2016 Jason Brownlee. All Rights Reserved Edition: v1.4 Contents Preface I Introduction 1 Welcome 1.1 Learn Python Machine Learning The Wrong Way 1.2 Machine Learning in Python 1. 3 What This book is not 22266 1.4 Summary I Lessons 2 Python Ecosystem for Machine Learning 9 2.1 Python 2.2 SciP 2.3 scikit-learn 2.4 Python Ecosystem Installation 11 2.5 Summary 13 3 Crash Course in Python and scipy 14 3.1 Python Crash Course 3.2 NumPy Crash Course 19 3.3 Matplotlib Crash Course 21 3.4 Pandas crash coursc 23 3.5 Summary 25 4 How To Load Machine Learning Data 26 4.1 Considerations When Loading csv data 26 4.2 Pima Indians dataset 27 4.3 Load CSV Files with the Python Standard Library 27 4.4 Load csv files with NumP 28 4.5 Load CSV Files with Pandas 2 4.6 Summary 5 Understand Your Data With Descriptive Statistics 31 5.1 Peek at Your data 31 5.2 Dimensions of your data 5.3 Data Type For Each Attribute 33 5.4 Descriptive Statistics 33 5.5 Class Distribution(Classification Only) 5.6 Correlations between Attributes 5.7 Skew of Univariate Distributions 5.8 Tips To Remember 5.9 Summary 37 6 Undcrstand Your data with visualization 38 6.1 Univariate plots 38 6.2 Multivariate Plots 41 6.3 Summary 7 Prepare Your Data For Machine Learning 47 7. 1 Need For Data Pre-processing ..47 7.2 Data Transforms 7.3 Rescale data 48 7. 4 Standardizc Data 7.5 Normalize data 50 7.6 Binarize Data(Make Binary) 50 7.7 Summary 51 8 Feature Selection For Machine learning 52 8.1 Feature Selection 52 8.2 Univariate Selection 53 8.3 Recursive feature elimination 53 8.4 Principal Component Analysis 54 8.5 Feature Importance 5 8.6 Summary 56 9 Evaluate the Performance of Machine Learning Algorithms with Resampling 57 9.1 Evaluate Machine Learning Algorithms 57 9.2 Split into Train and Test Sets 9.3 K-fold Cross validation 59 9. 4 Leave one out cross validation 59 9.5 Repeated Random Test-Train Splits 9.6 What Techniques to Use When 61 9. 7 Summary 61 10 Machinc Learning Algorithm Pcrformance Metrics 62 10.1 Algorithm Evaluation metrics 10.2 Classification Metrics 63 10.3 Regression Metrics 67 10.4 Uninary 69 1 Spot-Check Classification Algorithms 70 11. 1 Algorithm Spot-Checking 11.2 Algorithms Overview 71 11.3 Linear Machine Learning Algorithms 11. 4 Nonlinear Machine Learning Algorithms 11.5 Summary 75 12 Spot-Check Regression Algorithms 76 12.1 Algorithms Overview 76 12.2 Linear Machine Learning algorithms 12.3 Nonlinear Machine Learning Algorithms 12.4 Summary 13 Compare Machine Learning algorithms 83 13. 1 Choose The Best Machine Learning Model 13.2 Compare Machine Learning Algorithms Consistently 13.3 Summary 14 Automate Machine Learning Workflows with Pipelines 87 14.1 Automating Machine Learning WorkFlows 87 14.2 Data Preparation and modeling Pipeline 87 14.3 Feature Extraction and Modeling Pipeline 14.4 Summary 15 Improve Performance with Ensembles 91 15.1 Combine models into ensemble predictions 91 15.2 Bagging Algorithms 92 15. 3 Boosting algorithms .94 15.4 Voting ensemble 96 15.5 Summary 97 16 Improve Performance with Algorithm Tuning 98 16.1 Machine Learning algorithm Parameters 98 16.2 Grid Search Parameter Tuning 16.3 Random Search Parameter Tuning 99 16. 4 Summary 100 7 Save and Load Machine Learning Models 101 17. 1 Finalize Your Model with pickle 101 17.2 Finalize Your Model with Joblib 102 17.3 Tips for Finalizing Your Model 103 17.1 Summary 103 III Projects 105 18 Predictive Modeling Project Template 106 18.1 Practice Machine Learning With Projects 106 18.2 Machine Learning Project Template in Pytho 18.3 Machine Learning Project Template Step on 107 108 18.4 Tips For Using The Template Well ..110 18.5Su ummary 110 19 Your First Machine Learning Project in Python Step-By-Step 111 19.1 Thc Hello World of Machinc Learning 111 19.2 Load The data 112 19.3 Summarize the Dataset 113 19.1 Data Visualization 115 19.5 Evaluate some algorithms 118 19.6 Makc prcdictions 121 19.7 Summary 122 20 Regression Machine Learning Case Study Project 123 20.1 Problem Definition ..123 20.2 Load the dataset 124 20.3 Analyze Data ..125 20.1 Data Visualizations 20.5 Validation Dataset .,133 20.6 Evaluate Algorithms: Basclinc 134 20.7 Evaluate Algorithms: Standardization 136 20.8 Improve Results With Tuning 138 20.9 Ensemble Methods 139 20.10Tune ensemble methods 141 20.11 Finalize model 142 20.12Summary .143 21 Binary Classification Machine Learning Case Study Project 144 21.1 Problem Dcfinition 144 21.2 Load the dataset 44 14 21.3 Analyze Data 145 21.4 Validation Dataset .152 21.5 Evaluate Algorithms: Baseline ..153 21.6 Evaluate Algorithms: Standardize Data 155 21.7 Algorithm Tuning 157 21. 8 Ensemble methods 21.9 Finalize model 161 21. 10Summary 162 22 More Predictive Modeling Projects 163 22.1 Build And Maintain Recipes 163 22.2 Small Projects on Small Datasets 163 22.3 Competitive Machine Learning 164 22.4 Summary 164 IV Conclusions 166 23 How Far You have come 167 24 Getting More Help 168 24.1 General Advice 168 24.2 Help with Python 168 24.3 Help With SciPy and NumPy 169 24.4 Help With Matplotlib 169 24.5 Help With Pandas ..169 24.6 Hclp With scikit-Icarn .170 Preface I think Python is an amazing platform for machinc learning. There arc so many algorithms and so much power ready to use. I am often asked the question: Hou do you use Python for machine learning This book is my definitive answer to that question. It contains my very best knowledge and ideas on how to work through predictive modeling machine learning projects using the Python ecosystem. It is the book that I am also going to use as a refresher at the start of a new project. Im really proud of this book and i hope that you find it a useful companion on your machine learning journey with Python Jason brownlee Melbourne. australia Part i Introduction

...展开详情
img
  • 至尊王者

    成功上传501个资源即可获取

关注 私信 TA的资源

上传资源赚积分,得勋章
相关内容推荐