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Machine Learning with Python for Everyone
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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow Concepts 2nd Edition
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts 2nd Edition $5.00
Pearson

Machine Learning with Python for Everyone

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Author(s)

Mark Fenner

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Pearson

ISBN-10

0134845625

ISBN-13

978-0134845623

Pages

588

Language

English

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1st edition | August 16, 2019

File Size

7 MB

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$50

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ISBN: 978-0134845623 SKU: EB1353 Category: Computer Science Tags: AI, Artificial Intelligence, Computer Science, Machine Learning, Machine Learning with Python for Everyone, ML, Programming, Python
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Description

Machine Learning with Python for Everyone

Machine Learning with Python for Everyone (Addison-Wesley Data & Analytics Series) 1st Edition:

The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python

Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning.
Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use.

  • Understand machine learning algorithms, models, and core machine learning concepts
  • Classify examples with classifiers, and quantify examples with regressors
  • Realistically assess performance of machine learning systems
  • Use feature engineering to smooth rough data into useful forms
  • Chain multiple components into one system and tune its performance
  • Apply machine learning techniques to images and text
  • Connect the core concepts to neural networks and graphical models
  • Leverage the Python scikit-learn library and other powerful tools

Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.


Additional ISBNs:

∗ eText ISBN: 0134845641, 978-0134845647, 9780134845647

Table of Contents

Contents

Cover
About This E-Book
Half Title
Series Page
Title Page
Copyright Page
Dedication
Contents
Foreword
Preface
Audience
Approach
Overview
Acknowledgments
Publisher’s Note
About the Author
Part I: First Steps
1. Let’s Discuss Learning
1.1 Welcome
1.2 Scope, Terminology, Prediction, and Data
1.3 Putting the Machine in Machine Learning
1.4 Examples of Learning Systems
1.5 Evaluating Learning Systems
1.6 A Process for Building Learning Systems
1.7 Assumptions and Reality of Learning
1.8 End-of-Chapter Material
2. Some Technical Background
2.1 About Our Setup
2.2 The Need for Mathematical Language
2.3 Our Software for Tackling Machine Learning
2.4 Probability
2.5 Linear Combinations, Weighted Sums, and Dot Products
2.6 A Geometric View: Points in Space
2.7 Notation and the Plus-One Trick
2.8 Getting Groovy, Breaking the Straight-Jacket, and Nonlinearity
2.9 NumPy versus “All the Maths”
2.10 Floating-Point Issues
2.11 EOC
3. Predicting Categories: Getting Started with Classification
3.1 Classification Tasks
3.2 A Simple Classification Dataset
3.3 Training and Testing: Don’t Teach to the Test
3.4 Evaluation: Grading the Exam
3.5 Simple Classifier #1: Nearest Neighbors, Long Distance Relationships, and Assumptions
3.6 Simple Classifier #2: Naive Bayes, Probability, and Broken Promises
3.7 Simplistic Evaluation of Classifiers
3.8 EOC
4. Predicting Numerical Values: Getting Started with Regression
4.1 A Simple Regression Dataset
4.2 Nearest-Neighbors Regression and Summary Statistics
4.3 Linear Regression and Errors
4.4 Optimization: Picking the Best Answer
4.5 Simple Evaluation and Comparison of Regressors
4.6 EOC
Part II: Evaluation
5. Evaluating and Comparing Learners
5.1 Evaluation and Why Less Is More
5.2 Terminology for Learning Phases
5.3 Major Tom, There’s Something Wrong: Overfitting and Underfitting
5.4 From Errors to Costs
5.5 (Re)Sampling: Making More from Less
5.6 Break-It-Down: Deconstructing Error into Bias and Variance
5.7 Graphical Evaluation and Comparison
5.8 Comparing Learners with Cross-Validation
5.9 EOC
6. Evaluating Classifiers
6.1 Baseline Classifiers
6.2 Beyond Accuracy: Metrics for Classification
6.3 ROC Curves
6.4 Another Take on Multiclass: One-versus-One
6.5 Precision-Recall Curves
6.6 Cumulative Response and Lift Curves
6.7 More Sophisticated Evaluation of Classifiers: Take Two
6.8 EOC
7. Evaluating Regressors
7.1 Baseline Regressors
7.2 Additional Measures for Regression
7.3 Residual Plots
7.4 A First Look at Standardization
7.5 Evaluating Regressors in a More Sophisticated Way: Take Two
7.6 EOC
Part III: More Methods and Fundamentals
8. More Classification Methods
8.1 Revisiting Classification
8.2 Decision Trees
8.3 Support Vector Classifiers
8.4 Logistic Regression
8.5 Discriminant Analysis
8.6 Assumptions, Biases, and Classifiers
8.7 Comparison of Classifiers: Take Three
8.8 EOC
9. More Regression Methods
9.1 Linear Regression in the Penalty Box: Regularization
9.2 Support Vector Regression
9.3 Piecewise Constant Regression
9.4 Regression Trees
9.5 Comparison of Regressors: Take Three
9.6 EOC
10. Manual Feature Engineering: Manipulating Data for Fun and Profit
10.1 Feature Engineering Terminology and Motivation
10.2 Feature Selection and Data Reduction: Taking out the Trash
10.3 Feature Scaling
10.4 Discretization
10.5 Categorical Coding
10.6 Relationships and Interactions
10.7 Target Manipulations
10.8 EOC
11. Tuning Hyperparameters and Pipelines
11.1 Models, Parameters, Hyperparameters
11.2 Tuning Hyperparameters
11.3 Down the Recursive Rabbit Hole: Nested Cross-Validation
11.4 Pipelines
11.5 Pipelines and Tuning Together
11.6 EOC
Part IV: Adding Complexity
12. Combining Learners
12.1 Ensembles
12.2 Voting Ensembles
12.3 Bagging and Random Forests
12.4 Boosting
12.5 Comparing the Tree-Ensemble Methods
12.6 EOC
13. Models That Engineer Features for Us
13.1 Feature Selection
13.2 Feature Construction with Kernels
13.3 Principal Components Analysis: An Unsupervised Technique
13.4 EOC
14. Feature Engineering for Domains: Domain-Specific Learning
14.1 Working with Text
14.2 Clustering
14.3 Working with Images
14.4 EOC
15. Connections, Extensions, and Further Directions
15.1 Optimization
15.2 Linear Regression from Raw Materials
15.3 Building Logistic Regression from Raw Materials
15.4 SVM from Raw Materials
15.5 Neural Networks
15.6 Probabilistic Graphical Models
15.7 EOC
A. mlwpy.py Listing
Index
Code Snippets

About the Author

Mark Fenner

Dr. Mark Fenner, owner of Fenner Training and Consulting, LLC, has taught computing and mathematics to diverse adult audiences since 1999, and holds a PhD in computer science. His research has included design, implementation, and performance of machine learning and numerical algorithms; developing learning systems to detect user anomalies; and probabilistic modeling of protein function.

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