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10 produkter

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10 produkter

Regression Analysis with Python
Discover everything you need to know about the art of regression analysis with Python, and change how you view data
AvAlberto Boschetti,Luca Massaron
E-bok
Engelska, 2016
480 kr
Läs direkt efter köp
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 analysis
Who 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 data
In 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.
Python Data Science Essentials
AvAlberto Boschetti,Luca Massaron
Häftad, Engelska, 2015
584 kr
Skickas inom 5-8 vardagar
Regression Analysis with Python: Learn the art of regression analysis with Python
AvLuca Massaron,Alberto Boschetti
Häftad, Engelska, 2016
649 kr
Skickas inom 5-8 vardagar
Large Scale Machine Learning with Python
Learn to build powerful machine learning models quickly and deploy large-scale predictive applications
AvLuca Massaron,Alberto Boschettim. fl.
Häftad, Engelska, 2016
730 kr
Skickas inom 5-8 vardagar
Learn to build powerful machine learning models quickly and deploy large-scale predictive applicationsKey Features[*]Design, engineer and deploy scalable machine learning solutions with the power of Python[*]Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework[*]Build state-of-the-art models and develop personalized recommendations to perform machine learning at scaleBook DescriptionLarge 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.What you will learn[*]Apply the most scalable machine learning algorithms[*]Work with modern state-of-the-art large-scale machine learning techniques[*]Increase predictive accuracy with deep learning and scalable data-handling techniques[*]Improve your work by combining the MapReduce framework with Spark[*]Build powerful ensembles at scale[*] Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machineWho this book is forThis 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.
Large Scale Machine Learning with Python
Learn to build powerful machine learning models quickly and deploy large-scale predictive applications
AvBastiaan Sjardin,Alberto Boschettim. fl.
E-bok
Engelska, 2016
533 kr
Läs direkt efter köp
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 scale
Who 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 machine
In 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.
Python Data Science Essentials - Second Edition: Learn the fundamentals of Data Science with Python
AvAlberto Boschetti,Luca Massaron
Häftad, Engelska, 2016
649 kr
Skickas inom 5-8 vardagar
Python Data Science Essentials
Learn the fundamentals of Data Science with Python
AvAlberto Boschetti,Luca Massaron
E-bok
Engelska, 2016
473 kr
Läs direkt efter köp
Become an efficient data science practitioner by understanding Python\''s key concepts
Key Features
[*] Quickly get familiar with data science using Python 3.5[*] Save time (and effort) with all the essential tools explained[*] Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience
Book Description
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.
What you will learn
[*] Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux[*] Get data ready for your data science project[*] Manipulate, fix, and explore data in order to solve data science problems[*] Set up an experimental pipeline to test your data science hypotheses[*] Choose the most effective and scalable learning algorithm for your data science tasks[*] Optimize your machine learning models to get the best performance[*] Explore and cluster graphs, taking advantage of interconnections and links in your data
Who 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.
TensorFlow Deep Learning Projects: 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning
AvAlberto Boschetti,Luca Massaron
Häftad, Engelska, 2018
585 kr
Skickas inom 5-8 vardagar
Python Data Science Essentials
A practitioner's guide covering essential data science principles, tools, and techniques
AvAlberto Boschetti,Luca Massaronm. fl.
Häftad, Engelska, 2018
649 kr
Skickas inom 5-8 vardagar
Gain useful insights from your data using popular data science toolsKey FeaturesA one-stop guide to Python libraries such as pandas and NumPyComprehensive coverage of data science operations such as data cleaning and data manipulationChoose scalable learning algorithms for your data science tasksBook DescriptionFully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn.The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost.By the end of the book, you will have gained a complete overview of the 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 usersWhat you will learnSet up your data science toolbox on Windows, Mac, and LinuxUse the core machine learning methods offered by the scikit-learn libraryManipulate, fix, and explore data to solve data science problemsLearn advanced explorative and manipulative techniques to solve data operationsOptimize your machine learning models for optimized performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataWho this book is forIf you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.
Reinforcement Learning Workshop
Learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems
AvAlessandro Palmas,Emanuele Ghelfim. fl.
Häftad, Engelska, 2020
519 kr
Skickas
Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guideKey FeaturesUse TensorFlow to write reinforcement learning agents for performing challenging tasksLearn how to solve finite Markov decision problemsTrain models to understand popular video games like BreakoutBook DescriptionVarious intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models.Starting with an introduction to RL, youÔÇÖll be guided through different RL environments and frameworks. YouÔÇÖll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once youÔÇÖve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, youÔÇÖll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, youÔÇÖll find out when to use a policy-based method to tackle an RL problem.By the end of The Reinforcement Learning Workshop, youÔÇÖll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning.What you will learnUse OpenAI Gym as a framework to implement RL environmentsFind out how to define and implement reward functionExplore Markov chain, Markov decision process, and the Bellman equationDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference LearningUnderstand the multi-armed bandit problem and explore various strategies to solve itBuild a deep Q model network for playing the video game BreakoutWho this book is forIf you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.

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Alberto Boschetti – författare

Regression Analysis with Python

Python Data Science Essentials

Regression Analysis with Python: Learn the art of regression analysis with Python

Large Scale Machine Learning with Python

Large Scale Machine Learning with Python

Python Data Science Essentials - Second Edition: Learn the fundamentals of Data Science with Python

Python Data Science Essentials

TensorFlow Deep Learning Projects: 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning

Python Data Science Essentials

Reinforcement Learning Workshop

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