Building Machine Learning Systems with Python

Description

This book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems. Abstract: This book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems Content: Cover Copyright Credits About the Authors About the Reviewers www.PacktPub.com 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) What to do when you are stuck Getting started Introduction to NumPy, SciPy, and matplotlib Installing Python Chewing data efficiently with NumPy and intelligently with SciPy Learning NumPy Indexing Handling nonexisting values Comparing the runtime Learning SciPy Our first (tiny) application of machine learning. Reading in the dataPreprocessing and cleaning the data Choosing the right model and learning algorithm Before building our first model ... Starting with a simple straight line Towards some advanced stuff 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 Building our first classification model Evaluation -- holding out data and cross-validation Building more complex classifiers. A more complex dataset and a more complex classifierLearning about the Seeds dataset Features and feature engineering Nearest neighbor classification Classifying with scikit-learn Looking at the decision boundaries Binary and multiclass classification Summary Chapter 3: Clustering -- Finding Related Posts Measuring the relatedness of posts How not to do it How to do it Preprocessing -- similarity measured as a similar number of common words Converting raw text into a bag of words Counting words Normalizing word count vectors Removing less important words Stemming. Stop words on steroidsOur achievements and goals Clustering K-means Getting test data to evaluate our ideas on Clustering posts Solving our initial challenge Another look at noise Tweaking the parameters Summary Chapter 4: Topic Modeling Latent Dirichlet allocation Building a topic model Comparing documents by topics Modeling the whole of Wikipedia Choosing the number of topics Summary Chapter 5: Classification -- 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 chunksPreselection and processing of attributes Defining what is a good answer Creating our first classifier Starting with kNN Engineering the features Training the classifier Measuring the classifier's performance Designing more features Deciding how to improve Bias-variance and their tradeoff Fixing high bias Fixing high variance High bias 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.