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 recipe-based guide
This practical tutorial tackles real-world computing problems through a rigorous and effective approach
Build state-of-the-art 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 real-world 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 command-line 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 real-world problems
Understand how to perform market segmentation using unsupervised learning
Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test
Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms
Increase predictive accuracy with deep learning and scalable data-handling techniques
Work with modern state-of-the-art large-scale 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 data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving 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 real-world 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 real-world problems. Through this comprehensive course, you’ll learn to create the most effective machine learning techniques from scratch and more!
Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.
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 cross-validation
- 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 k-means 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 k-nearest neighbors classifier
- Constructing a k-nearest 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 bag-of-words 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 OpenCV-Python
- 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. Semi-Supervised 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 Scikit-learn
- 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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