| Description |
1 online resource (394 pages) |
| Contents |
Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Python Machine Learning; Machine learning and Python -- a dream team; What the book will teach you -- and what it will not; How to best read this book; What to do when you are stuck; Getting started; Introduction to NumPy, SciPy, Matplotlib, and TensorFlow; Installing Python; Chewing data efficiently with NumPy and intelligently with SciPy; Learning NumPy; Indexing; Handling nonexistent values; Comparing the runtime; Learning SciPy; Fundamentals of machine learning |
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Asking a questionGetting answers; Our first (tiny) application of machine learning; Reading in the data; Preprocessing and cleaning the data; Choosing the right model and learning algorithm; Before we build our first model; Starting with a simple straight line; Toward more complex models; Stepping back to go forward -- another look at our data; Training and testing; Answering our initial question; Summary; Chapter 2: Classifying with Real-World Examples; The Iris dataset; Visualization is a good first step; Classifying with scikit-learn; Building our first classification model |
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Evaluation -- holding out data and cross-validationHow to measure and compare classifiers; A more complex dataset and the nearest-neighbor classifier; Learning about the seeds dataset; Features and feature engineering; Nearest neighbor classification; Looking at the decision boundaries; Which classifier to use; Summary; Chapter 3: Regression; Predicting house prices with regression; Multidimensional regression; Cross-validation for regression; Penalized or regularized regression; L1 and L2 penalties; Using Lasso or ElasticNet in scikit-learn; Visualizing the Lasso path |
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P-greater-than-N scenariosAn example based on text documents; Setting hyperparameters in a principled way; Regression with TensorFlow; Summary; Chapter 4: Classification I -- Detecting Poor Answers; Sketching our roadmap; Learning to classify classy answers; Tuning the instance; Tuning the classifier; Fetching the data; Slimming the data down to chewable chunks; Preselecting and processing attributes; Defining what a good answer is; Creating our first classifier; Engineering the features; Training the classifier; Measuring the classifier's performance; Designing more features |
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Deciding how to improve the performanceBias, variance and their trade-off; Fixing high bias; Fixing high variance; High or low bias?; Using logistic regression; A bit of math with a small example; Applying logistic regression to our post-classification problem; Looking behind accuracy -- precision and recall; Slimming the classifier; Ship it!; Classification using Tensorflow; Summary; Chapter 5: Dimensionality Reduction; Sketching our roadmap; Selecting features; Detecting redundant features using filters; Correlation; Mutual information; Asking the model about the features using wrappers |
| Summary |
Machine learning allows models or systems to learn without being explicitly programmed. You will see how to use the best of libraries support such as scikit-learn, Tensorflow and much more to build efficient smart systems |
| Notes |
Other feature selection methods |
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Print version record |
| Subject |
Python (Computer program language)
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Machine learning.
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Artificial intelligence.
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Artificial Intelligence
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Machine Learning
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artificial intelligence.
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Artificial intelligence.
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Neural networks & fuzzy systems.
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Information architecture.
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Database design & theory.
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Computers -- Intelligence (AI) & Semantics.
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Computers -- Neural Networks.
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Computers -- Data Modeling & Design.
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Artificial intelligence
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Machine learning
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Python (Computer program language)
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| Form |
Electronic book
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| Author |
Richert, Wilhelm
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Brucher, Matthieu
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| ISBN |
9781788622226 |
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1788622227 |
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9781788623223 |
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1788623223 |
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