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Preface I think Python is an amazing platform for machine learning. There are so many algorithms and so much power ready to use. I am often asked the question: How 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. I’m 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 2016
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Jason Brownlee
Machine Learning Mastery With Python
Understand Your Data, Create Accurate Models and
Work Projects End-To-End
i
Machi ne Learning Mastery With Python
Copyright 2016 Jason Brownlee. All Rights Reserved.
Edition: v1.4
Contents
Preface iii
I Introduction 1
1 Welcome 2
1.1 Learn Python Machine Learning The Wrong Way ................. 2
1.2 Machine Learning in Python ............................. 2
1.3 What This Book is Not ................................ 6
1.4 Summary ....................................... 6
II Lessons 8
2 Python Ecosystem for Machine Learning 9
2.1 Python ......................................... 9
2.2 SciPy .......................................... 10
2.3 scikit-learn ....................................... 10
2.4 Python Ecosystem Installation ............................ 11
2.5 Summary ....................................... 13
3 Crash Course in Python and SciPy 14
3.1 Python Crash Course ................................. 14
3.2 NumPy Crash Course ................................. 19
3.3 Matplotlib Crash Course ............................... 21
3.4 Pandas Crash Course ................................. 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 St an d a r d Library ................ 27
4.4 Load CSV Files with NumPy ............................ 28
4.5 Load CSV Files with Pandas ............................. 28
4.6 Summary ....................................... 29
ii
iii
5 Understand Your Data With Descriptive Statistics 31
5.1 Peek at Your Data .................................. 31
5.2 Dimensions of Your Data ............................... 32
5.3 Data Type For Each Attribute ............................ 33
5.4 Descriptive Statistics ................................. 33
5.5 Class Distribution (Classification Only) ....................... 34
5.6 Correlations Between Attributes ........................... 35
5.7 Skew of Univariate Distributions ........................... 36
5.8 Tips To Remember .................................. 36
5.9 Summary ....................................... 37
6 Understand Your Data With Visualization 38
6.1 Univariate Plots .................................... 38
6.2 Multivariate Plots ................................... 41
6.3 Summary ....................................... 45
7 Prepare Your Data For Machine Learning 47
7.1 Need For Data Pre-p rocessing ............................ 47
7.2 Data Transforms ................................... 47
7.3 Rescale Data ..................................... 48
7.4 Standardize Data ................................... 49
7.5 Normalize Data .................................... 50
7.6 Binarize Data (Make Binary) ............................ 50
7.7 Summary ....................................... 51
8 Feature Selection For Machine Lea rni ng 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 .................................. 55
8.6 Summary ....................................... 56
9 Evaluate the Per fo rm a nce of Machine Learning Algorithms with Resampling 57
9.1 Evaluate Machi n e Learning Algorithms ....................... 57
9.2 Split into Train and Test Sets ............................ 58
9.3 K-fold Cross Validation ................................ 59
9.4 Leave One Out Cross Validation ........................... 59
9.5 Repeated Random Test- Train Splits ......................... 60
9.6 What Techniques to Use When ........................... 61
9.7 Summary ....................................... 61
10 Machine Learning Algorithm Performance Metrics 62
10.1 Algorithm Evaluation Metrics ............................ 62
10.2 Classification Metrics ................................. 63
10.3 Regression Metrics .................................. 67
10.4 Summary ....................................... 69
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