Download This PDF Book: Python Machine Learning: Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics 1st Edition by Sebastian Raschka, for free.
About This Book
Leverage Python’s most powerful open-source libraries for deep learning, data wrangling, and data visualization
Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms
Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets
Who This Book Is For
If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.
What You Will Learn
Explore how to use different machine learning models to ask different questions of your data
Learn how to build neural networks using Keras and Theano
Find out how to write clean and elegant Python code that will optimize the strength of your algorithms
Discover how to embed your machine learning model in a web application for increased accessibility
Predict continuous target outcomes using regression analysis
Uncover hidden patterns and structures in data with clustering
Organize data using effective pre-processing techniques
Get to grips with sentiment analysis to delve deeper into textual and social media data
In Detail
Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace.
Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.
Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable.
Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you’ll soon be able to answer some of the most important questions facing you and your organization.
Style and approach
Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions.
It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
About the Author
Sebastian Raschka is a PhD student at Michigan State University, who develops new computational methods in the field of computational biology. He has been ranked as the number one most influential data scientist on GitHub by Analytics Vidhya.
He has a yearlong experience in Python programming and he has conducted several seminars on the practical applications of data science and machine learning.
Talking and writing about data science, machine learning, and Python really motivated Sebastian to write this book in order to help people develop data-driven solutions without necessarily needing to have a machine learning background.
He has also actively contributed to open source projects and methods that he implemented, which are now successfully used in machine learning competitions, such as Kaggle. In his free time, he works on models for sports predictions, and if he is not in front of the computer, he enjoys playing sports.
Table of Contents:
Chapter 1: Giving Computers the Ability to Learn from Data
Chapter 2: Training Machine Learning Algorithms for Classification
Chapter 3: A Tour of Machine Learning Classifiers Using Scikit-learn
Chapter 4: Building Good Training Sets – Data Preprocessing
Chapter 5: Compressing Data via Dimensionality Reduction
Chapter 6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning
Chapter 7: Combining Different Models for Ensemble Learning
Chapter 8: Applying Machine Learning to Sentiment Analysis
Chapter 9: Embedding a Machine Learning Model into a Web Application
Chapter 10: Predicting Continuous Target Variables with Regression Analysis
Chapter 11: Working with Unlabeled Data – Clustering Analysis
Chapter 12: Training Artificial Neural Networks for Image Recognition
Chapter 13: Parallelizing Neural Network Training with Theano
About The Book:
Publisher : Packt Publishing; 1st edition (September 23, 2015)
Language : English
Pages : 456
File : PDF, 20 MB
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