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To borrow includes Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, Introduction to Machine Learning with Python: A Guide for Data Scientists and Python Machine Learning.

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Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

by: Wes McKinney
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.

Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing.

  • Use the IPython interactive shell as your primary development environment
  • Learn basic and advanced NumPy (Numerical Python) features
  • Get started with data analysis tools in the pandas library
  • Use high-performance tools to load, clean, transform, merge, and reshape data
  • Create scatter plots and static or interactive visualizations with matplotlib
  • Apply the pandas groupby facility to slice, dice, and summarize datasets
  • Measure data by points in time, whether it's specific instances, fixed periods, or intervals
  • Learn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

Introduction to Machine Learning with Python: A Guide for Data Scientists

by: Andreas C. Mueller, Sarah Guido
Introduction to Machine Learning with Python: A Guide for Data Scientists

Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data. Introduction to Machine Learning with Python teaches you the basics of machine learning and provides a thorough hands-on understanding of the subject.

You'll learn important machine learning concepts and algorithms, when to use them, and how to use them. The book covers a machine learning workflow: data preprocessing and working with data, training algorithms, evaluating results, and implementing those algorithms into a production-level system.

Python Machine Learning

by: Sebastian Raschka
Python Machine Learning

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics

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 Pylearn 2 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 Pylearn2, 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.


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