Alberto Boschetti – författare
481 kr
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Learn the art of regression analysis with Python
About This Book
Become competent at implementing regression analysis in PythonSolve some of the complex data science problems related to predicting outcomesGet to grips with various types of regression for effective data analysisWho This Book Is For
The book targets Python developers, with a basic understanding of data science, statistics, and math, who want to learn how to do regression analysis on a dataset. It is beneficial if you have some knowledge of statistics and data science.
What You Will Learn
Format a dataset for regression and evaluate its performanceApply multiple linear regression to real-world problemsLearn to classify training pointsCreate an observation matrix, using different techniques of data analysis and cleaningApply several techniques to decrease (and eventually fix) any overfitting problemLearn to scale linear models to a big dataset and deal with incremental dataIn Detail
Regression is the process of learning relationships between inputs and continuous outputs from example data, which enables predictions for novel inputs. There are many kinds of regression algorithms, and the aim of this book is to explain which is the right one to use for each set of problems and how to prepare real-world data for it. With this book you will learn to define a simple regression problem and evaluate its performance. The book will help you understand how to properly parse a dataset, clean it, and create an output matrix optimally built for regression. You will begin with a simple regression algorithm to solve some data science problems and then progress to more complex algorithms. The book will enable you to use regression models to predict outcomes and take critical business decisions. Through the book, you will gain knowledge to use Python for building fast better linear models and to apply the results in Python or in any computer language you prefer.
Style and approach
This is a practical tutorial-based book. You will be given an example problem and then supplied with the relevant code and how to walk through it. The details are provided in a step by step manner, followed by a thorough explanation of the math underlying the solution. This approach will help you leverage your own data using the same techniques.
585 kr
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651 kr
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732 kr
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535 kr
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Learn to build powerful machine learning models quickly and deploy large-scale predictive applications
About This Book
Design, engineer and deploy scalable machine learning solutions with the power of PythonTake command of Hadoop and Spark with Python for effective machine learning on a map reduce frameworkBuild state-of-the-art models and develop personalized recommendations to perform machine learning at scaleWho This Book Is For
This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful.
What You Will Learn
Apply the most scalable machine learning algorithmsWork with modern state-of-the-art large-scale machine learning techniquesIncrease predictive accuracy with deep learning and scalable data-handling techniquesImprove your work by combining the MapReduce framework with SparkBuild powerful ensembles at scaleUse data streams to train linear and non-linear predictive models from extremely large datasets using a single machineIn Detail
Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy.
Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.
Style and Approach
This efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly.
Large-scale machine learning tackles a different issue to what is currently on the market. Those working with Hadoop clusters and in data intensive environments can now learn effective ways of building powerful machine learning models from prototype to production.
This book is written in a style that programmers from other languages (R, Julia, Java, Matlab) can follow.
651 kr
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474 kr
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Become an efficient data science practitioner by understanding Python''s key concepts
About This Book
Quickly get familiar with data science using Python 3.5Save time (and effort) with all the essential tools explainedCreate effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experienceWho This Book Is For
If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills.
What You Will Learn
Set up your data science toolbox using a Python scientific environment on Windows, Mac, and LinuxGet data ready for your data science projectManipulate, fix, and explore data in order to solve data science problemsSet up an experimental pipeline to test your data science hypothesesChoose the most effective and scalable learning algorithm for your data science tasksOptimize your machine learning models to get the best performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataIn Detail
Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow.
Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users.
Style and approach
The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
585 kr
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651 kr
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520 kr
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