Book description
Learn to solve challenging data science problems by building powerful machine learning models using Python
About This Book
 Understand which algorithms to use in a given context with the help of this exciting recipebased guide
 This practical tutorial tackles realworld computing problems through a rigorous and effective approach
 Build stateoftheart models and develop personalized recommendations to perform machine learning at scale
Who This Book Is For
This Learning Path is for Python programmers who are looking to use machine learning algorithms to create realworld applications. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. Experience with Python, Jupyter Notebooks, and commandline execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected.
What You Will Learn
 Use predictive modeling and apply it to realworld problems
 Understand how to perform market segmentation using unsupervised learning
 Apply your newfound skills to solve real problems, through clearlyexplained code for every technique and test
 Compete with top data scientists by gaining a practical and theoretical understanding of cuttingedge deep learning algorithms
 Increase predictive accuracy with deep learning and scalable datahandling techniques
 Work with modern stateoftheart largescale machine learning techniques
 Learn to use Python code to implement a range of machine learning algorithms and techniques
In Detail
Machine learning is increasingly spreading in the modern datadriven world. It is used extensively across many fields such as search engines, robotics, selfdriving cars, and more. Machine learning is transforming the way we understand and interact with the world around us.
In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve realworld problems and use Python to implement these algorithms.
The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and feature engineering.
The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.
This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice.
This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:
 Python Machine Learning Cookbook by Prateek Joshi
 Advanced Machine Learning with Python by John Hearty
 Large Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca Massaron
Style and approach
This course is a smooth learning path that will teach you how to get started with Python machine learning for the real world, and develop solutions to realworld problems. Through this comprehensive course, you'll learn to create the most effective machine learning techniques from scratch and more!
Publisher resources
Table of contents

Python: Real World Machine Learning
 Table of Contents
 Python: Real World Machine Learning
 Python: Real World Machine Learning
 Credits
 Preface

I. Module 1

1. The Realm of Supervised Learning
 Introduction
 Preprocessing data using different techniques
 Label encoding
 Building a linear regressor
 Computing regression accuracy
 Achieving model persistence
 Building a ridge regressor
 Building a polynomial regressor
 Estimating housing prices
 Computing the relative importance of features
 Estimating bicycle demand distribution

2. Constructing a Classifier
 Introduction
 Building a simple classifier
 Building a logistic regression classifier
 Building a Naive Bayes classifier
 Splitting the dataset for training and testing
 Evaluating the accuracy using crossvalidation
 Visualizing the confusion matrix
 Extracting the performance report
 Evaluating cars based on their characteristics
 Extracting validation curves
 Extracting learning curves
 Estimating the income bracket
 3. Predictive Modeling

4. Clustering with Unsupervised Learning
 Introduction
 Clustering data using the kmeans algorithm
 Compressing an image using vector quantization
 Building a Mean Shift clustering model
 Grouping data using agglomerative clustering
 Evaluating the performance of clustering algorithms
 Automatically estimating the number of clusters using DBSCAN algorithm
 Finding patterns in stock market data
 Building a customer segmentation model

5. Building Recommendation Engines
 Introduction
 Building function compositions for data processing
 Building machine learning pipelines
 Finding the nearest neighbors
 Constructing a knearest neighbors classifier
 Constructing a knearest neighbors regressor
 Computing the Euclidean distance score
 Computing the Pearson correlation score
 Finding similar users in the dataset
 Generating movie recommendations

6. Analyzing Text Data
 Introduction
 Preprocessing data using tokenization
 Stemming text data
 Converting text to its base form using lemmatization
 Dividing text using chunking
 Building a bagofwords model
 Building a text classifier
 Identifying the gender
 Analyzing the sentiment of a sentence
 Identifying patterns in text using topic modeling
 7. Speech Recognition

8. Dissecting Time Series and Sequential Data
 Introduction
 Transforming data into the time series format
 Slicing time series data
 Operating on time series data
 Extracting statistics from time series data
 Building Hidden Markov Models for sequential data
 Building Conditional Random Fields for sequential text data
 Analyzing stock market data using Hidden Markov Models

9. Image Content Analysis
 Introduction
 Operating on images using OpenCVPython
 Detecting edges
 Histogram equalization
 Detecting corners
 Detecting SIFT feature points
 Building a Star feature detector
 Creating features using visual codebook and vector quantization
 Training an image classifier using Extremely Random Forests
 Building an object recognizer

10. Biometric Face Recognition
 Introduction
 Capturing and processing video from a webcam
 Building a face detector using Haar cascades
 Building eye and nose detectors
 Performing Principal Components Analysis
 Performing Kernel Principal Components Analysis
 Performing blind source separation
 Building a face recognizer using Local Binary Patterns Histogram

11. Deep Neural Networks
 Introduction
 Building a perceptron
 Building a single layer neural network
 Building a deep neural network
 Creating a vector quantizer
 Building a recurrent neural network for sequential data analysis
 Visualizing the characters in an optical character recognition database
 Building an optical character recognizer using neural networks
 12. Visualizing Data

1. The Realm of Supervised Learning

II. Module 2
 1. Unsupervised Machine Learning
 2. Deep Belief Networks
 3. Stacked Denoising Autoencoders
 4. Convolutional Neural Networks
 5. SemiSupervised Learning
 6. Text Feature Engineering
 7. Feature Engineering Part II
 8. Ensemble Methods
 9. Additional Python Machine Learning Tools
 A. Chapter Code Requirements

III. Module 3
 1. First Steps to Scalability
 2. Scalable Learning in Scikitlearn
 3. Fast SVM Implementations

4. Neural Networks and Deep Learning
 The neural network architecture
 Neural networks and regularization
 Neural networks and hyperparameter optimization
 Neural networks and decision boundaries
 Deep learning at scale with H2O
 Deep learning and unsupervised pretraining
 Deep learning with theanets
 Autoencoders and unsupervised learning
 Summary
 5. Deep Learning with TensorFlow
 6. Classification and Regression Trees at Scale
 7. Unsupervised Learning at Scale
 8. Distributed Environments – Hadoop and Spark
 9. Practical Machine Learning with Spark
 A. Introduction to GPUs and Theano
 A. Bibliography
 Index
Product information
 Title: Python: Real World Machine Learning
 Author(s):
 Release date: November 2016
 Publisher(s): Packt Publishing
 ISBN: 9781787123212
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